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一、前言

《基于大数据的饮水健康行为分析与可视化研究》是一套面向大四计算机专业毕业设计的饮水健康数据分析系统，核心是把日常饮水记录放到Hadoop HDFS中存储，再通过Spark、Spark SQL完成清洗、聚合和统计分析，Python版本用Django做后端，Java版本用Spring Boot做后端，结果落到MySQL，前端用Vue、ElementUI、Echarts、HTML、CSS、JavaScript和jQuery做页面展示。系统首页负责整体导航，数据大屏集中展示饮水健康关键指标，饮水健康数据模块维护原始记录，活动程度分析观察不同活动量下的饮水差异，体格因素分析结合身高体重等指标看饮水需求，人口结构分析按年龄、性别等维度做对比，水分状况分析判断每日补水是否合理，综合洞察分析汇总多个分析结果，天气影响分析结合天气情况看饮水变化，个人信息和修改密码用于账号维护。整体重点在大数据处理和可视化呈现，适合作为计算机专业毕设参考。

二、开发环境

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

三、系统界面展示

• 基于大数据的饮水健康行为分析与可视化研究系统界面展示：
  
  
  
  
  
  
  
  
  

四、部分代码设计

• 项目实战-代码参考：

frompyspark.sqlimportSparkSessionfrompyspark.sql.functionsimportcol,whenfromdjango.httpimportJsonResponse spark=SparkSession.builder.appName("DrinkingHealthBehaviorAnalysis").master("local[*]").config("spark.sql.shuffle.partitions","4").getOrCreate()drink_df=spark.read.format("jdbc").option("url","jdbc:mysql://localhost:3306/drinking_health").option("dbtable","drink_record").option("user","root").option("password","123456").load()weather_df=spark.read.format("jdbc").option("url","jdbc:mysql://localhost:3306/drinking_health").option("dbtable","weather_record").option("user","root").option("password","123456").load()defactivity_analysis(request):records=drink_df.select("user_id","drink_ml","activity_level","record_date").na.drop()activity_stat=records.groupBy("activity_level").agg({"drink_ml":"avg","user_id":"count"}).withColumnRenamed("avg(drink_ml)","avg_drink").withColumnRenamed("count(user_id)","user_count")result=activity_stat.orderBy("activity_level").collect()data=[{"activity_level":row["activity_level"],"avg_drink":round(row["avg_drink"],2),"user_count":row["user_count"]}forrowinresult]total_avg=records.agg({"drink_ml":"avg"}).collect()[0][0]high_activity=records.filter(col("activity_level")=="高").agg({"drink_ml":"avg"}).collect()[0][0]low_activity=records.filter(col("activity_level")=="低").agg({"drink_ml":"avg"}).collect()[0][0]gap=round((high_activityor0)-(low_activityor0),2)suggestion="活动量高的人群饮水均值更高"ifgap>0else"活动量与饮水均值差异不明显"top_users=records.groupBy("user_id").agg({"drink_ml":"sum"}).withColumnRenamed("sum(drink_ml)","sum_drink").orderBy(col("sum_drink").desc()).limit(10).collect()top_data=[{"user_id":row["user_id"],"sum_drink":round(row["sum_drink"],2)}forrowintop_users]max_activity=max(data,key=lambdax:x["avg_drink"])ifdataelse{}min_activity=min(data,key=lambdax:x["avg_drink"])ifdataelse{}returnJsonResponse({"code":200,"data":data,"total_avg":round(total_avgor0,2),"gap":gap,"suggestion":suggestion,"top_data":top_data,"max_activity":max_activity,"min_activity":min_activity})defwater_status_analysis(request):records=drink_df.select("user_id","drink_ml","weight","height","record_date").na.drop()records=records.withColumn("bmi",col("weight")/((col("height")/100)*(col("height")/100)))daily=records.groupBy("user_id","record_date").agg({"drink_ml":"sum"}).withColumnRenamed("sum(drink_ml)","daily_ml")daily=daily.withColumn("target_ml",when(col("bmi")<18.5,2000).when(col("bmi")<24,1800).otherwise(2200))status=daily.withColumn("status",when(col("daily_ml")<col("target_ml")*0.8,"偏低").when(col("daily_ml")>col("target_ml")*1.2,"偏高").otherwise("正常"))low_count=status.filter(col("status")=="偏低").count()normal_count=status.filter(col("status")=="正常").count()high_count=status.filter(col("status")=="偏高").count()total=low_count+normal_count+high_count normal_rate=round(normal_count/total*100,2)iftotalelse0chart_data=[{"name":"偏低","value":low_count},{"name":"正常","value":normal_count},{"name":"偏高","value":high_count}]abnormal=status.filter(col("status")!="正常").select("user_id","record_date","daily_ml","target_ml","status").limit(20).collect()abnormal_data=[{"user_id":row["user_id"],"record_date":str(row["record_date"]),"daily_ml":row["daily_ml"],"target_ml":row["target_ml"],"status":row["status"]}forrowinabnormal]avg_daily=daily.agg({"daily_ml":"avg"}).collect()[0][0]returnJsonResponse({"code":200,"chart_data":chart_data,"normal_rate":normal_rate,"abnormal_data":abnormal_data,"avg_daily":round(avg_dailyor0,2),"tip":"按BMI给出参考饮水量后判断水分状况"})defweather_analysis(request):join_df=drink_df.join(weather_df,on="record_date",how="inner")weather_stat=join_df.groupBy("weather_type").agg({"drink_ml":"avg","user_id":"count"}).withColumnRenamed("avg(drink_ml)","avg_drink").withColumnRenamed("count(user_id)","user_count")result=weather_stat.orderBy("weather_type").collect()data=[{"weather_type":row["weather_type"],"avg_drink":round(row["avg_drink"],2),"user_count":row["user_count"]}forrowinresult]weather_daily=join_df.groupBy("record_date","weather_type").agg({"drink_ml":"avg"}).withColumnRenamed("avg(drink_ml)","avg_drink")trend=weather_daily.orderBy("record_date").collect()trend_data=[{"date":str(row["record_date"]),"weather_type":row["weather_type"],"avg_drink":round(row["avg_drink"],2)}forrowintrend]hot_avg=join_df.filter(col("weather_type")=="高温").agg({"drink_ml":"avg"}).collect()[0][0]cold_avg=join_df.filter(col("weather_type")=="寒冷").agg({"drink_ml":"avg"}).collect()[0][0]diff=round((hot_avgor0)-(cold_avgor0),2)insight="高温天气饮水均值高于寒冷天气"ifdiff>0else"天气对饮水均值影响有限"max_weather=max(data,key=lambdax:x["avg_drink"])ifdataelse{}min_weather=min(data,key=lambdax:x["avg_drink"])ifdataelse{}rain_avg=join_df.filter(col("weather_type")=="雨天").agg({"drink_ml":"avg"}).collect()[0][0]rain_tip="雨天饮水均值也需要留意"if(rain_avgor0)<(hot_avgor0)else"雨天饮水均值相对平稳"returnJsonResponse({"code":200,"data":data,"trend_data":trend_data,"diff":diff,"insight":insight,"max_weather":max_weather,"min_weather":min_weather,"rain_tip":rain_tip})

五、论文参考

• 计算机毕业设计选题推荐-基于大数据的饮水健康行为分析与可视化研究系统-论文参考：
  

六、系统视频

• 基于大数据的饮水健康行为分析与可视化研究系统-项目视频：
  点击观看项目演示视频

结语

计算机毕业设计选题推荐:基于大数据的饮水健康行为分析与可视化研究|毕业设计选题|计算机毕设|选题推荐|毕设指导|项目定制|源码|高质量项目
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