选题迷茫+技术复杂+时间紧迫？健身房会员锻炼数据分析与可视化系统毕设通关指南|计算机毕业设计

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 基于大数据的健身房会员锻炼数据分析与可视化系统是一个综合运用Hadoop+Spark大数据技术栈的数据分析平台，专门针对健身房会员的锻炼行为进行深度挖掘和可视化展示。系统采用Python作为主要开发语言，结合Django框架构建后端服务，前端使用Vue+ElementUI+Echarts技术栈实现交互界面。通过Hadoop分布式存储架构管理海量会员锻炼数据，利用Spark进行大规模数据处理和分析，配合Spark SQL实现复杂查询操作。系统核心功能涵盖会员画像分析、行为偏好分析、锻炼效果分析和健康指标分析四大模块，能够从多维度解析会员的锻炼习惯、健身偏好、运动效果和身体状况变化。平台提供直观的数据可视化界面，通过Echarts图表库展现分析结果，帮助健身房管理者深入了解会员需求，优化服务质量，制定个性化健身方案。同时系统具备完整的用户管理、系统公告、个人信息维护等基础功能，为健身房的数字化运营提供全面的数据支撑和决策依据。

三、基于大数据的健身房会员锻炼数据分析与可视化系统-视频解说

选题迷茫+技术复杂+时间紧迫？健身房会员锻炼数据分析与可视化系统毕设通关指南|计算机毕业设计

四、基于大数据的健身房会员锻炼数据分析与可视化系统-功能展示

五、基于大数据的健身房会员锻炼数据分析与可视化系统-代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
import pandas as pd
import numpy as np

spark = SparkSession.builder.appName("GymDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()

def member_portrait_analysis():
    member_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gym_db").option("dbtable", "gym_members").option("user", "root").option("password", "123456").load()
    workout_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gym_db").option("dbtable", "workout_records").option("user", "root").option("password", "123456").load()
    merged_df = member_df.join(workout_df, "member_id", "inner")
    age_group_df = merged_df.withColumn("age_group", when(col("age") < 25, "青年").when(col("age") < 35, "中年").otherwise("中老年"))
    gender_stats = age_group_df.groupBy("gender", "age_group").agg(count("*").alias("member_count"), avg("workout_duration").alias("avg_duration"), sum("calories_burned").alias("total_calories"))
    workout_frequency = merged_df.groupBy("member_id").agg(count("*").alias("workout_times"), sum("workout_duration").alias("total_duration"))
    frequency_distribution = workout_frequency.withColumn("frequency_level", when(col("workout_times") < 10, "低频").when(col("workout_times") < 30, "中频").otherwise("高频"))
    final_portrait = frequency_distribution.groupBy("frequency_level").agg(count("*").alias("member_count"), avg("total_duration").alias("avg_total_duration"))
    member_preferences = merged_df.groupBy("member_id", "equipment_type").agg(sum("workout_duration").alias("equipment_duration"))
    preference_ranking = member_preferences.withColumn("rank", row_number().over(Window.partitionBy("member_id").orderBy(desc("equipment_duration"))))
    top_preferences = preference_ranking.filter(col("rank") <= 3)
    bmi_analysis = member_df.withColumn("bmi", col("weight") / (col("height") / 100) ** 2).withColumn("bmi_category", when(col("bmi") < 18.5, "偏瘦").when(col("bmi") < 24, "正常").when(col("bmi") < 28, "超重").otherwise("肥胖"))
    bmi_stats = bmi_analysis.groupBy("bmi_category", "gender").agg(count("*").alias("count"), avg("age").alias("avg_age"))
    return {"gender_age_stats": gender_stats.collect(), "frequency_distribution": final_portrait.collect(), "top_preferences": top_preferences.collect(), "bmi_analysis": bmi_stats.collect()}

def behavior_preference_analysis():
    workout_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gym_db").option("dbtable", "workout_records").option("user", "root").option("password", "123456").load()
    time_pattern = workout_df.withColumn("hour", hour(col("workout_time"))).withColumn("weekday", dayofweek(col("workout_time")))
    time_preference = time_pattern.withColumn("time_period", when(col("hour") < 9, "早晨").when(col("hour") < 14, "上午").when(col("hour") < 18, "下午").otherwise("晚上"))
    time_stats = time_preference.groupBy("time_period", "weekday").agg(count("*").alias("workout_count"), avg("workout_duration").alias("avg_duration"))
    equipment_usage = workout_df.groupBy("equipment_type").agg(count("*").alias("usage_count"), sum("workout_duration").alias("total_duration"), avg("calories_burned").alias("avg_calories"))
    equipment_ranking = equipment_usage.orderBy(desc("usage_count"))
    member_equipment = workout_df.groupBy("member_id", "equipment_type").agg(count("*").alias("usage_times"), sum("workout_duration").alias("total_time"))
    equipment_diversity = member_equipment.groupBy("member_id").agg(count("equipment_type").alias("equipment_variety"), sum("total_time").alias("member_total_time"))
    diversity_stats = equipment_diversity.withColumn("diversity_level", when(col("equipment_variety") < 3, "单一型").when(col("equipment_variety") < 6, "多样型").otherwise("全能型"))
    diversity_distribution = diversity_stats.groupBy("diversity_level").agg(count("*").alias("member_count"), avg("member_total_time").alias("avg_workout_time"))
    workout_intensity = workout_df.withColumn("intensity_level", when(col("calories_burned") / col("workout_duration") < 8, "低强度").when(col("calories_burned") / col("workout_duration") < 15, "中强度").otherwise("高强度"))
    intensity_analysis = workout_intensity.groupBy("intensity_level", "equipment_type").agg(count("*").alias("workout_sessions"), avg("workout_duration").alias("avg_duration"))
    return {"time_preference": time_stats.collect(), "equipment_ranking": equipment_ranking.collect(), "diversity_distribution": diversity_distribution.collect(), "intensity_analysis": intensity_analysis.collect()}

def workout_effect_analysis():
    member_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gym_db").option("dbtable", "gym_members").option("user", "root").option("password", "123456").load()
    health_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gym_db").option("dbtable", "health_indicators").option("user", "root").option("password", "123456").load()
    workout_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/gym_db").option("dbtable", "workout_records").option("user", "root").option("password", "123456").load()
    member_progress = health_df.withColumn("weight_change", col("current_weight") - col("initial_weight")).withColumn("muscle_change", col("current_muscle_mass") - col("initial_muscle_mass"))
    progress_stats = member_progress.withColumn("weight_trend", when(col("weight_change") < -2, "明显减重").when(col("weight_change") < 2, "体重稳定").otherwise("体重增加"))
    weight_analysis = progress_stats.groupBy("weight_trend").agg(count("*").alias("member_count"), avg("weight_change").alias("avg_weight_change"), avg("muscle_change").alias("avg_muscle_change"))
    workout_effectiveness = workout_df.groupBy("member_id").agg(sum("calories_burned").alias("total_calories"), count("*").alias("workout_sessions"), sum("workout_duration").alias("total_minutes"))
    effectiveness_merged = workout_effectiveness.join(member_progress, "member_id", "inner")
    effectiveness_analysis = effectiveness_merged.withColumn("calories_per_kg_lost", when(col("weight_change") < 0, col("total_calories") / abs(col("weight_change"))).otherwise(0))
    calorie_efficiency = effectiveness_analysis.filter(col("weight_change") < 0).agg(avg("calories_per_kg_lost").alias("avg_calories_per_kg"))
    workout_consistency = workout_df.withColumn("workout_date", to_date(col("workout_time"))).groupBy("member_id").agg(count("workout_date").alias("workout_days"), countDistinct("workout_date").alias("unique_days"))
    consistency_ratio = workout_consistency.withColumn("consistency_score", col("unique_days") / col("workout_days") * 100)
    consistency_levels = consistency_ratio.withColumn("consistency_level", when(col("consistency_score") > 80, "高度坚持").when(col("consistency_score") > 60, "中度坚持").otherwise("坚持不足"))
    consistency_stats = consistency_levels.groupBy("consistency_level").agg(count("*").alias("member_count"), avg("consistency_score").alias("avg_score"))
    return {"weight_analysis": weight_analysis.collect(), "calorie_efficiency": calorie_efficiency.collect(), "consistency_stats": consistency_stats.collect()}

六、基于大数据的健身房会员锻炼数据分析与可视化系统-文档展示

七、END

• 💛💛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