计算机毕 设 指导师
⭐⭐个人介绍：自己非常喜欢研究技术问题！专业做Java、Python、小程序、安卓、大数据、爬虫、Golang、大屏等实战项目。
大家都可点赞、收藏、关注、有问题都可留言评论交流
实战项目：有源码或者技术上的问题欢迎在评论区一起讨论交流！
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求！你也可以在个人主页上↑↑联系我~~
⚡⚡获取源码主页-->：计算机毕设指导师
人体体能活动能量消耗分析系统- 简介
基于大数据的人体体能活动能量消耗分析与可视化系统是一个综合运用Hadoop分布式存储、Spark大数据处理引擎以及现代Web开发技术构建的智能化数据分析平台。该系统采用Python作为主要开发语言，结合Django Web框架搭建后端服务架构，前端运用Vue.js配合ElementUI组件库和ECharts可视化库实现用户交互界面。系统核心功能围绕EEHPA人体能量消耗数据集展开，通过Hadoop HDFS实现海量数据的分布式存储管理，利用Spark SQL和Spark Core引擎执行复杂的数据处理任务。平台提供多维度的数据分析能力，包括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、生理指标与能量消耗的关联性研究以及多因素综合分析等核心模块。系统通过Pandas和NumPy进行精确的数值计算和统计分析，结合MySQL数据库存储分析结果和用户信息，最终通过直观的图表和可视化界面向用户展示分析结果，为体能活动能量消耗研究提供了一个功能完善、技术先进的数据分析解决方案。
人体体能活动能量消耗分析系统-技术
开发语言：java或Python
数据库：MySQL
系统架构：B/S
前端：Vue+ElementUI+HTML+CSS+JavaScript+jQuery+Echarts
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)
人体体能活动能量消耗分析系统- 背景
随着现代生活方式的转变和健康意识的不断提升，人体体能活动与能量消耗的科学研究逐渐成为运动生理学、营养学和健康管理领域的重要课题。传统的能量消耗测量方法往往依赖于实验室环境下的间接量热法或心率监测等单一指标，难以全面反映不同个体在各类日常活动中的真实能量消耗情况。近年来，可穿戴设备的普及和传感器技术的发展使得连续性、多指标的生理数据采集成为可能，产生了大量包含心率、呼吸频率、氧气摄入量等多维生理指标的体能活动数据。这些海量数据蕴含着丰富的生理活动规律和个体差异信息，但传统的数据处理方法面临着存储容量限制、计算效率低下和分析维度单一等挑战。大数据技术的成熟为解决这些问题提供了新的技术路径，Hadoop和Spark等分布式计算框架能够高效处理TB级别的生理监测数据，为深入挖掘人体能量消耗规律创造了条件。
本课题的研究对于推动人体能量消耗科学研究的技术进步具有积极的实际意义。通过构建基于大数据技术的分析系统，能够处理和分析传统方法难以处理的大规模多维生理数据，为研究人员提供更加全面和精确的数据分析工具。系统实现的多角度关联分析功能有助于发现性别、年龄、BMI等人口统计学特征与能量消耗之间的潜在关系，为个性化健康管理和运动处方制定提供数据支撑。从技术应用角度来看，该系统展示了大数据技术在健康医疗领域的实际应用价值，验证了Hadoop和Spark在生理数据处理中的可行性和有效性。虽然作为毕业设计项目，系统在数据规模和功能复杂度上相对有限，但其设计思路和技术架构为后续更大规模的健康大数据研究项目提供了参考价值。同时，系统的可视化分析功能降低了复杂数据分析结果的理解门槛，使得非专业人员也能够直观地了解不同活动类型和个体特征对能量消耗的影响，这对促进全民健康意识的提升和科学健身观念的普及具有一定的积极作用。
人体体能活动能量消耗分析系统-视频展示
人体体能活动能量消耗分析系统-图片展示
人体体能活动能量消耗分析系统-代码展示
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import mysql.connector
from decimal import Decimal
import logging
spark = SparkSession.builder.appName("EnergyConsumptionAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def analyze_gender_energy_consumption(request):
if request.method == 'POST':
try:
data = json.loads(request.body)
dataset_path = data.get('dataset_path', '/data/eehpa_dataset.csv')
df = spark.read.option("header", "true").option("inferSchema", "true").csv(dataset_path)
df.createOrReplaceTempView("energy_data")
gender_analysis = spark.sql("""
SELECT gender,
COUNT(*) as total_records,
ROUND(AVG(EEm), 3) as avg_energy_consumption,
ROUND(STDDEV(EEm), 3) as stddev_energy,
ROUND(MIN(EEm), 3) as min_energy,
ROUND(MAX(EEm), 3) as max_energy,
ROUND(PERCENTILE_APPROX(EEm, 0.25), 3) as q1_energy,
ROUND(PERCENTILE_APPROX(EEm, 0.5), 3) as median_energy,
ROUND(PERCENTILE_APPROX(EEm, 0.75), 3) as q3_energy
FROM energy_data
WHERE EEm IS NOT NULL AND gender IS NOT NULL
GROUP BY gender
ORDER BY avg_energy_consumption DESC
""")
result_df = gender_analysis.toPandas()
gender_comparison = spark.sql("""
SELECT
(SELECT AVG(EEm) FROM energy_data WHERE gender = 'male' AND EEm IS NOT NULL) as male_avg,
(SELECT AVG(EEm) FROM energy_data WHERE gender = 'female' AND EEm IS NOT NULL) as female_avg
""").collect()[0]
male_avg = float(gender_comparison['male_avg']) if gender_comparison['male_avg'] else 0
female_avg = float(gender_comparison['female_avg']) if gender_comparison['female_avg'] else 0
gender_diff_percent = round(((male_avg - female_avg) / female_avg) * 100, 2) if female_avg > 0 else 0
age_gender_analysis = spark.sql("""
SELECT gender,
CASE
WHEN age < 30 THEN '20-29'
WHEN age < 40 THEN '30-39'
WHEN age < 50 THEN '40-49'
WHEN age < 60 THEN '50-59'
ELSE '60+'
END as age_group,
ROUND(AVG(EEm), 3) as avg_energy,
COUNT(*) as sample_count
FROM energy_data
WHERE EEm IS NOT NULL AND gender IS NOT NULL AND age IS NOT NULL
GROUP BY gender, age_group
ORDER BY gender, age_group
""")
age_gender_df = age_gender_analysis.toPandas()
connection = mysql.connector.connect(host='localhost', database='energy_analysis', user='root', password='password')
cursor = connection.cursor()
cursor.execute("DELETE FROM gender_analysis_results WHERE analysis_date = CURDATE()")
for _, row in result_df.iterrows():
insert_query = """INSERT INTO gender_analysis_results
(gender, total_records, avg_energy_consumption, stddev_energy,
min_energy, max_energy, q1_energy, median_energy, q3_energy, analysis_date)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, CURDATE())"""
cursor.execute(insert_query, (row['gender'], int(row['total_records']),
float(row['avg_energy_consumption']), float(row['stddev_energy']),
float(row['min_energy']), float(row['max_energy']),
float(row['q1_energy']), float(row['median_energy']), float(row['q3_energy'])))
connection.commit()
cursor.close()
connection.close()
response_data = {
'status': 'success',
'gender_statistics': result_df.to_dict('records'),
'gender_difference_percent': gender_diff_percent,
'age_gender_analysis': age_gender_df.to_dict('records'),
'male_avg_energy': male_avg,
'female_avg_energy': female_avg,
'total_samples': int(df.count())
}
return JsonResponse(response_data)
except Exception as e:
logging.error(f"Gender analysis error: {str(e)}")
return JsonResponse({'status': 'error', 'message': str(e)}, status=500)
@csrf_exempt
def analyze_activity_energy_patterns(request):
if request.method == 'POST':
try:
data = json.loads(request.body)
dataset_path = data.get('dataset_path', '/data/eehpa_dataset.csv')
df = spark.read.option("header", "true").option("inferSchema", "true").csv(dataset_path)
df.createOrReplaceTempView("activity_data")
activity_energy_analysis = spark.sql("""
SELECT original_activity_labels as activity_type,
COUNT(*) as total_instances,
ROUND(AVG(EEm), 3) as avg_energy_consumption,
ROUND(STDDEV(EEm), 3) as energy_stddev,
ROUND(AVG(METS), 3) as avg_mets,
ROUND(MIN(EEm), 3) as min_energy,
ROUND(MAX(EEm), 3) as max_energy,
ROUND(PERCENTILE_APPROX(EEm, 0.95), 3) as energy_95th_percentile
FROM activity_data
WHERE EEm IS NOT NULL AND original_activity_labels IS NOT NULL
GROUP BY original_activity_labels
HAVING COUNT(*) >= 10
ORDER BY avg_energy_consumption DESC
""")
activity_df = activity_energy_analysis.toPandas()
static_vs_dynamic = spark.sql("""
SELECT
CASE
WHEN LOWER(original_activity_labels) RLIKE '(sit|lying|rest|sleep|desk)' THEN 'static'
WHEN LOWER(original_activity_labels) RLIKE '(walk|run|jump|climb|exercise|sport)' THEN 'dynamic'
ELSE 'mixed'
END as activity_category,
COUNT(*) as total_count,
ROUND(AVG(EEm), 3) as avg_energy,
ROUND(STDDEV(EEm), 3) as stddev_energy,
ROUND(AVG(HR), 3) as avg_heart_rate
FROM activity_data
WHERE EEm IS NOT NULL AND original_activity_labels IS NOT NULL
GROUP BY activity_category
ORDER BY avg_energy DESC
""")
category_df = static_vs_dynamic.toPandas()
activity_intensity_analysis = spark.sql("""
SELECT original_activity_labels,
CASE
WHEN METS < 3 THEN 'low_intensity'
WHEN METS < 6 THEN 'moderate_intensity'
ELSE 'high_intensity'
END as intensity_level,
ROUND(AVG(EEm), 3) as avg_energy,
ROUND(AVG(HR), 3) as avg_hr,
COUNT(*) as sample_count
FROM activity_data
WHERE EEm IS NOT NULL AND METS IS NOT NULL AND original_activity_labels IS NOT NULL
GROUP BY original_activity_labels, intensity_level
HAVING COUNT(*) >= 5
ORDER BY original_activity_labels, intensity_level
""")
intensity_df = activity_intensity_analysis.toPandas()
gender_activity_comparison = spark.sql("""
SELECT original_activity_labels,
gender,
ROUND(AVG(EEm), 3) as avg_energy,
ROUND(STDDEV(EEm), 3) as stddev_energy,
COUNT(*) as sample_size
FROM activity_data
WHERE EEm IS NOT NULL AND gender IS NOT NULL AND original_activity_labels IS NOT NULL
GROUP BY original_activity_labels, gender
HAVING COUNT(*) >= 5
ORDER BY original_activity_labels, gender
""")
gender_activity_df = gender_activity_comparison.toPandas()
connection = mysql.connector.connect(host='localhost', database='energy_analysis', user='root', password='password')
cursor = connection.cursor()
cursor.execute("DELETE FROM activity_analysis_results WHERE analysis_date = CURDATE()")
for _, row in activity_df.iterrows():
insert_query = """INSERT INTO activity_analysis_results
(activity_type, total_instances, avg_energy_consumption, energy_stddev,
avg_mets, min_energy, max_energy, energy_95th_percentile, analysis_date)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, CURDATE())"""
cursor.execute(insert_query, (row['activity_type'], int(row['total_instances']),
float(row['avg_energy_consumption']), float(row['energy_stddev']),
float(row['avg_mets']), float(row['min_energy']),
float(row['max_energy']), float(row['energy_95th_percentile'])))
connection.commit()
cursor.close()
connection.close()
energy_variance_coefficient = activity_df['energy_stddev'].mean() / activity_df['avg_energy_consumption'].mean()
response_data = {
'status': 'success',
'activity_energy_analysis': activity_df.to_dict('records'),
'static_vs_dynamic_comparison': category_df.to_dict('records'),
'activity_intensity_patterns': intensity_df.to_dict('records'),
'gender_activity_differences': gender_activity_df.to_dict('records'),
'energy_variance_coefficient': round(energy_variance_coefficient, 4),
'total_activity_types': len(activity_df)
}
return JsonResponse(response_data)
except Exception as e:
logging.error(f"Activity analysis error: {str(e)}")
return JsonResponse({'status': 'error', 'message': str(e)}, status=500)
@csrf_exempt
def analyze_physiological_energy_correlation(request):
if request.method == 'POST':
try:
data = json.loads(request.body)
dataset_path = data.get('dataset_path', '/data/eehpa_dataset.csv')
df = spark.read.option("header", "true").option("inferSchema", "true").csv(dataset_path)
df.createOrReplaceTempView("physio_data")
correlation_analysis = spark.sql("""
SELECT
COUNT(*) as total_records,
ROUND(CORR(HR, EEm), 4) as hr_energy_correlation,
ROUND(CORR(VO2, EEm), 4) as vo2_energy_correlation,
ROUND(CORR(VCO2, EEm), 4) as vco2_energy_correlation,
ROUND(CORR(VE, EEm), 4) as ve_energy_correlation,
ROUND(CORR(Qt, EEm), 4) as qt_energy_correlation,
ROUND(CORR(SV, EEm), 4) as sv_energy_correlation,
ROUND(CORR(R, EEm), 4) as r_energy_correlation
FROM physio_data
WHERE EEm IS NOT NULL AND HR IS NOT NULL AND VO2 IS NOT NULL
""")
correlation_df = correlation_analysis.toPandas()
hr_zone_analysis = spark.sql("""
SELECT
CASE
WHEN HR < 100 THEN 'resting (< 100 bpm)'
WHEN HR < 120 THEN 'light (100-119 bpm)'
WHEN HR < 140 THEN 'moderate (120-139 bpm)'
WHEN HR < 160 THEN 'vigorous (140-159 bpm)'
ELSE 'maximum (>= 160 bpm)'
END as hr_zone,
COUNT(*) as zone_count,
ROUND(AVG(EEm), 3) as avg_energy_consumption,
ROUND(AVG(VO2), 3) as avg_vo2,
ROUND(AVG(VCO2), 3) as avg_vco2,
ROUND(STDDEV(EEm), 3) as energy_stddev
FROM physio_data
WHERE EEm IS NOT NULL AND HR IS NOT NULL
GROUP BY hr_zone
ORDER BY AVG(HR)
""")
hr_zone_df = hr_zone_analysis.toPandas()
respiratory_efficiency = spark.sql("""
SELECT original_activity_labels,
ROUND(AVG(VE_VO2), 3) as avg_ventilatory_efficiency,
ROUND(AVG(VE_VCO2), 3) as avg_vco2_efficiency,
ROUND(AVG(R), 3) as avg_respiratory_quotient,
ROUND(AVG(EEm), 3) as avg_energy,
COUNT(*) as sample_count
FROM physio_data
WHERE EEm IS NOT NULL AND VE_VO2 IS NOT NULL AND original_activity_labels IS NOT NULL
GROUP BY original_activity_labels
HAVING COUNT(*) >= 8
ORDER BY avg_energy DESC
""")
respiratory_df = respiratory_efficiency.toPandas()
cardiac_output_analysis = spark.sql("""
SELECT
CASE
WHEN Qt < 15 THEN 'low_output'
WHEN Qt < 25 THEN 'normal_output'
ELSE 'high_output'
END as cardiac_output_category,
ROUND(AVG(EEm), 3) as avg_energy,
ROUND(AVG(HR), 3) as avg_hr,
ROUND(AVG(SV), 3) as avg_stroke_volume,
ROUND(CORR(SV, EEm), 4) as sv_energy_corr,
COUNT(*) as category_count
FROM physio_data
WHERE EEm IS NOT NULL AND Qt IS NOT NULL AND SV IS NOT NULL
GROUP BY cardiac_output_category
ORDER BY avg_energy
""")
cardiac_df = cardiac_output_analysis.toPandas()
oxygen_pulse_analysis = spark.sql("""
SELECT gender,
CASE
WHEN age < 40 THEN 'young_adult'
WHEN age < 60 THEN 'middle_aged'
ELSE 'older_adult'
END as age_category,
ROUND(AVG(VO2_HR), 3) as avg_oxygen_pulse,
ROUND(AVG(EEm), 3) as avg_energy,
ROUND(CORR(VO2_HR, EEm), 4) as oxygen_pulse_energy_corr,
COUNT(*) as group_size
FROM physio_data
WHERE EEm IS NOT NULL AND VO2_HR IS NOT NULL AND gender IS NOT NULL AND age IS NOT NULL
GROUP BY gender, age_category
ORDER BY gender, age_category
""")
oxygen_pulse_df = oxygen_pulse_analysis.toPandas()
connection = mysql.connector.connect(host='localhost', database='energy_analysis', user='root', password='password')
cursor = connection.cursor()
cursor.execute("DELETE FROM physiological_correlations WHERE analysis_date = CURDATE()")
for _, row in correlation_df.iterrows():
insert_query = """INSERT INTO physiological_correlations
(total_records, hr_energy_corr, vo2_energy_corr, vco2_energy_corr,
ve_energy_corr, qt_energy_corr, sv_energy_corr, r_energy_corr, analysis_date)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s, CURDATE())"""
cursor.execute(insert_query, (int(row['total_records']), float(row['hr_energy_correlation']),
float(row['vo2_energy_correlation']), float(row['vco2_energy_correlation']),
float(row['ve_energy_correlation']), float(row['qt_energy_correlation']),
float(row['sv_energy_correlation']), float(row['r_energy_correlation'])))
connection.commit()
cursor.close()
connection.close()
strongest_correlations = []
corr_cols = ['hr_energy_correlation', 'vo2_energy_correlation', 'vco2_energy_correlation']
for col in corr_cols:
if not correlation_df[col].isna().iloc[0]:
strongest_correlations.append({'indicator': col.replace('_energy_correlation', '').upper(), 'correlation': float(correlation_df[col].iloc[0])})
strongest_correlations.sort(key=lambda x: abs(x['correlation']), reverse=True)
response_data = {
'status': 'success',
'physiological_correlations': correlation_df.to_dict('records')[0],
'heart_rate_zone_analysis': hr_zone_df.to_dict('records'),
'respiratory_efficiency_analysis': respiratory_df.to_dict('records'),
'cardiac_output_analysis': cardiac_df.to_dict('records'),
'oxygen_pulse_analysis': oxygen_pulse_df.to_dict('records'),
'strongest_correlations': strongest_correlations[:3],
'analysis_summary': f"Analyzed {int(correlation_df['total_records'].iloc[0])} physiological records"
}
return JsonResponse(response_data)
except Exception as e:
logging.error(f"Physiological analysis error: {str(e)}")
return JsonResponse({'status': 'error', 'message': str(e)}, status=500)
人体体能活动能量消耗分析系统-结语
Python+大数据框架计算机毕设：人体体能活动能量消耗分析系统从零到完整实现指南 毕业设计 选题推荐 数据分析 深度学习 数据挖掘
计算机毕设选题难+技术栈复杂？人体体能活动能量消耗数据分析系统一站式解决方案
如果你觉得本文有用，一键三连（点赞、评论、转发）欢迎关注我，就是对我最大支持~~
也期待在评论区或私信看到你的想法和建议，一起交流探讨！谢谢大家！
⚡⚡获取源码主页-->：计算机毕设指导师
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求！你也可以在个人主页上↑↑联系我~~
