前言
💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 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 计算机毕业设计选题 💜💜
一.开发工具简介
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
二.系统内容简介
基于Hadoop+Spark的人体体能活动能量消耗数据分析与可视化系统是一款专门针对人体运动数据进行深度挖掘和智能分析的大数据应用平台。该系统充分利用Hadoop分布式存储架构和Spark内存计算引擎的优势，对海量的人体体能活动数据进行高效处理和实时分析。系统采用Python+Django后端框架，结合Vue+ElementUI+Echarts前端技术栈，构建了完整的数据采集、存储、处理、分析和可视化展示流程。通过集成MySQL数据库管理用户信息和元数据，系统实现了个人中心管理、用户权限控制、体能数据录入与管理、多维度体能活动分析、人口统计学特征挖掘、设备实时监测数据处理、人体生理指标综合评估等核心功能模块。系统特别注重数据可视化展示，通过智能大屏实时呈现分析结果，为用户提供直观的数据洞察和决策支持，在体育科学研究、健康管理和运动指导等领域具有重要的应用价值。
三.系统功能演示
大数据毕业设计推荐：基于Hadoop+Spark的人体体能活动能量消耗数据分析与可视化系统|计算机毕业|毕设
四.系统界面展示
五.系统源码展示
from pyspark.sql import SparkSession
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 import View
import json
spark = SparkSession.builder.appName("BodyEnergyAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
class EnergyConsumptionAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
user_id = data.get('user_id')
date_range = data.get('date_range', 7)
activity_type = data.get('activity_type', 'all')
energy_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "energy_consumption").option("user", "root").option("password", "123456").load()
activity_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "activity_records").option("user", "root").option("password", "123456").load()
filtered_energy = energy_df.filter((col("user_id") == user_id) & (col("record_date") >= date_sub(current_date(), date_range)))
if activity_type != 'all':
filtered_energy = filtered_energy.filter(col("activity_type") == activity_type)
joined_df = filtered_energy.join(activity_df, "activity_id", "inner")
daily_consumption = joined_df.groupBy("record_date").agg(sum("calories_burned").alias("total_calories"), avg("heart_rate").alias("avg_heart_rate"), sum("duration_minutes").alias("total_duration"), count("*").alias("activity_count"))
activity_breakdown = joined_df.groupBy("activity_type").agg(sum("calories_burned").alias("type_calories"), avg("intensity_level").alias("avg_intensity"), count("*").alias("type_count"))
hourly_pattern = joined_df.groupBy(hour("start_time").alias("hour")).agg(sum("calories_burned").alias("hourly_calories"), avg("heart_rate").alias("hourly_heart_rate"))
metabolic_rate = joined_df.withColumn("metabolic_equivalent", when(col("intensity_level") == "low", 3.0).when(col("intensity_level") == "medium", 6.0).otherwise(9.0))
metabolic_analysis = metabolic_rate.groupBy("user_id").agg(avg("metabolic_equivalent").alias("avg_met"), sum("calories_burned").alias("total_calories"), avg("body_weight").alias("avg_weight"))
efficiency_score = metabolic_analysis.withColumn("efficiency_ratio", col("total_calories") / (col("avg_weight") * col("avg_met")))
daily_results = daily_consumption.orderBy("record_date").collect()
activity_results = activity_breakdown.orderBy(desc("type_calories")).collect()
hourly_results = hourly_pattern.orderBy("hour").collect()
efficiency_results = efficiency_score.collect()
response_data = {"daily_consumption": [row.asDict() for row in daily_results], "activity_breakdown": [row.asDict() for row in activity_results], "hourly_pattern": [row.asDict() for row in hourly_results], "efficiency_metrics": [row.asDict() for row in efficiency_results]}
return JsonResponse({"status": "success", "data": response_data})
class MultiDimensionalAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
analysis_type = data.get('analysis_type', 'comprehensive')
time_period = data.get('time_period', 30)
user_group = data.get('user_group', 'all')
energy_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "energy_consumption").option("user", "root").option("password", "123456").load()
user_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "user_profiles").option("user", "root").option("password", "123456").load()
physiological_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "physiological_data").option("user", "root").option("password", "123456").load()
comprehensive_df = energy_df.join(user_df, "user_id", "inner").join(physiological_df, "user_id", "inner")
filtered_df = comprehensive_df.filter(col("record_date") >= date_sub(current_date(), time_period))
if user_group != 'all':
age_ranges = {"young": [18, 30], "middle": [31, 50], "senior": [51, 80]}
if user_group in age_ranges:
min_age, max_age = age_ranges[user_group]
filtered_df = filtered_df.filter((col("age") >= min_age) & (col("age") <= max_age))
age_analysis = filtered_df.groupBy("age_group").agg(avg("calories_burned").alias("avg_calories"), avg("heart_rate").alias("avg_hr"), avg("duration_minutes").alias("avg_duration"), stddev("calories_burned").alias("calories_std"))
gender_analysis = filtered_df.groupBy("gender").agg(avg("calories_burned").alias("gender_avg_calories"), sum("calories_burned").alias("gender_total_calories"), avg("bmi").alias("avg_bmi"))
correlation_df = filtered_df.select("calories_burned", "heart_rate", "duration_minutes", "age", "weight", "height", "bmi")
correlation_pandas = correlation_df.toPandas()
correlation_matrix = correlation_pandas.corr()
performance_trend = filtered_df.withColumn("week_number", weekofyear("record_date")).groupBy("week_number").agg(avg("calories_burned").alias("weekly_avg_calories"), avg("heart_rate").alias("weekly_avg_hr"), count("*").alias("weekly_activities"))
efficiency_by_bmi = filtered_df.withColumn("bmi_category", when(col("bmi") < 18.5, "underweight").when(col("bmi") < 25, "normal").when(col("bmi") < 30, "overweight").otherwise("obese")).groupBy("bmi_category").agg(avg("calories_burned").alias("bmi_avg_calories"), avg("duration_minutes").alias("bmi_avg_duration"))
intensity_distribution = filtered_df.groupBy("intensity_level", "activity_type").agg(count("*").alias("frequency"), avg("calories_burned").alias("intensity_calories"))
age_results = age_analysis.collect()
gender_results = gender_analysis.collect()
trend_results = performance_trend.orderBy("week_number").collect()
bmi_results = efficiency_by_bmi.collect()
intensity_results = intensity_distribution.collect()
response_data = {"age_analysis": [row.asDict() for row in age_results], "gender_analysis": [row.asDict() for row in gender_results], "performance_trend": [row.asDict() for row in trend_results], "bmi_efficiency": [row.asDict() for row in bmi_results], "intensity_distribution": [row.asDict() for row in intensity_results], "correlation_matrix": correlation_matrix.to_dict()}
return JsonResponse({"status": "success", "data": response_data})
class RealTimeMonitoringView(View):
def post(self, request):
data = json.loads(request.body)
device_ids = data.get('device_ids', [])
monitoring_duration = data.get('duration_minutes', 60)
alert_threshold = data.get('alert_threshold', 180)
device_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "device_monitoring").option("user", "root").option("password", "123456").load()
realtime_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "realtime_data").option("user", "root").option("password", "123456").load()
current_time = current_timestamp()
time_window_start = current_time - expr(f"INTERVAL {monitoring_duration} MINUTES")
filtered_realtime = realtime_df.filter((col("timestamp") >= time_window_start) & (col("timestamp") <= current_time))
if device_ids:
filtered_realtime = filtered_realtime.filter(col("device_id").isin(device_ids))
device_joined = filtered_realtime.join(device_df, "device_id", "inner")
heart_rate_analysis = device_joined.groupBy("device_id", "user_id").agg(avg("heart_rate").alias("avg_heart_rate"), max("heart_rate").alias("max_heart_rate"), min("heart_rate").alias("min_heart_rate"), stddev("heart_rate").alias("hr_variability"))
calorie_tracking = device_joined.withColumn("minute_interval", date_trunc("minute", col("timestamp"))).groupBy("device_id", "minute_interval").agg(sum("calories_per_minute").alias("interval_calories"), avg("heart_rate").alias("interval_hr"))
activity_intensity = device_joined.withColumn("intensity_score", when(col("heart_rate") > alert_threshold, "high").when(col("heart_rate") > alert_threshold * 0.7, "medium").otherwise("low")).groupBy("device_id", "intensity_score").agg(count("*").alias("intensity_count"), avg("calories_per_minute").alias("avg_calorie_rate"))
anomaly_detection = device_joined.filter((col("heart_rate") > alert_threshold) | (col("heart_rate") < 60) | (col("calories_per_minute") > 20) | (col("calories_per_minute") < 0))
device_status = device_joined.groupBy("device_id").agg(max("timestamp").alias("last_update"), count("*").alias("data_points"), avg("battery_level").alias("avg_battery"))
current_metrics = device_joined.filter(col("timestamp") >= current_time - expr("INTERVAL 5 MINUTES")).groupBy("device_id").agg(last("heart_rate").alias("current_hr"), last("calories_per_minute").alias("current_calorie_rate"), last("step_count").alias("current_steps"))
performance_summary = device_joined.groupBy("user_id").agg(sum("calories_per_minute").alias("total_calories_tracked"), avg("heart_rate").alias("session_avg_hr"), max("heart_rate").alias("session_max_hr"), (max("step_count") - min("step_count")).alias("total_steps"))
hr_results = heart_rate_analysis.collect()
calorie_results = calorie_tracking.orderBy("minute_interval").collect()
intensity_results = activity_intensity.collect()
anomaly_results = anomaly_detection.collect()
status_results = device_status.collect()
current_results = current_metrics.collect()
summary_results = performance_summary.collect()
response_data = {"heart_rate_analysis": [row.asDict() for row in hr_results], "calorie_tracking": [row.asDict() for row in calorie_results], "intensity_analysis": [row.asDict() for row in intensity_results], "anomaly_alerts": [row.asDict() for row in anomaly_results], "device_status": [row.asDict() for row in status_results], "current_metrics": [row.asDict() for row in current_results], "performance_summary": [row.asDict() for row in summary_results]}
return JsonResponse({"status": "success", "data": response_data})
六.系统文档展示
结束
💛💛想说的话：感谢大家的关注与支持！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 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 计算机毕业设计选题 💜💜
