✨作者主页IT毕设梦工厂✨个人简介曾从事计算机专业培训教学擅长Java、Python、PHP、.NET、Node.js、GO、微信小程序、安卓Android等项目实战。接项目定制开发、代码讲解、答辩教学、文档编写、降重等。☑文末获取源码☑精彩专栏推荐⬇⬇⬇Java项目Python项目安卓项目微信小程序项目文章目录一、前言二、开发环境三、系统界面展示四、部分代码设计五、论文参考六、系统视频结语一、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HadoopSpark本次没用Hive支持定制开发语言PythonJava两个版本都支持后端框架DjangoSpring Boot(SpringSpringMVCMybatis)两个版本都支持前端VueElementUIEchartsHTMLCSSJavaScriptjQuery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 sparkSparkSession.builder.appName(DrinkingHealthBehaviorAnalysis).master(local[*]).config(spark.sql.shuffle.partitions,4).getOrCreate()drink_dfspark.read.format(jdbc).option(url,jdbc:mysql://localhost:3306/drinking_health).option(dbtable,drink_record).option(user,root).option(password,123456).load()weather_dfspark.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):recordsdrink_df.select(user_id,drink_ml,activity_level,record_date).na.drop()activity_statrecords.groupBy(activity_level).agg({drink_ml:avg,user_id:count}).withColumnRenamed(avg(drink_ml),avg_drink).withColumnRenamed(count(user_id),user_count)resultactivity_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_avgrecords.agg({drink_ml:avg}).collect()[0][0]high_activityrecords.filter(col(activity_level)高).agg({drink_ml:avg}).collect()[0][0]low_activityrecords.filter(col(activity_level)低).agg({drink_ml:avg}).collect()[0][0]gapround((high_activityor0)-(low_activityor0),2)suggestion活动量高的人群饮水均值更高ifgap0else活动量与饮水均值差异不明显top_usersrecords.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_activitymax(data,keylambdax:x[avg_drink])ifdataelse{}min_activitymin(data,keylambdax: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):recordsdrink_df.select(user_id,drink_ml,weight,height,record_date).na.drop()recordsrecords.withColumn(bmi,col(weight)/((col(height)/100)*(col(height)/100)))dailyrecords.groupBy(user_id,record_date).agg({drink_ml:sum}).withColumnRenamed(sum(drink_ml),daily_ml)dailydaily.withColumn(target_ml,when(col(bmi)18.5,2000).when(col(bmi)24,1800).otherwise(2200))statusdaily.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_countstatus.filter(col(status)偏低).count()normal_countstatus.filter(col(status)正常).count()high_countstatus.filter(col(status)偏高).count()totallow_countnormal_counthigh_count normal_rateround(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}]abnormalstatus.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_dailydaily.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_dfdrink_df.join(weather_df,onrecord_date,howinner)weather_statjoin_df.groupBy(weather_type).agg({drink_ml:avg,user_id:count}).withColumnRenamed(avg(drink_ml),avg_drink).withColumnRenamed(count(user_id),user_count)resultweather_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_dailyjoin_df.groupBy(record_date,weather_type).agg({drink_ml:avg}).withColumnRenamed(avg(drink_ml),avg_drink)trendweather_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_avgjoin_df.filter(col(weather_type)高温).agg({drink_ml:avg}).collect()[0][0]cold_avgjoin_df.filter(col(weather_type)寒冷).agg({drink_ml:avg}).collect()[0][0]diffround((hot_avgor0)-(cold_avgor0),2)insight高温天气饮水均值高于寒冷天气ifdiff0else天气对饮水均值影响有限max_weathermax(data,keylambdax:x[avg_drink])ifdataelse{}min_weathermin(data,keylambdax:x[avg_drink])ifdataelse{}rain_avgjoin_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|高质量项目大家可以帮忙点赞、收藏、关注、评论啦源码获取⬇⬇⬇精彩专栏推荐⬇⬇⬇Java项目Python项目安卓项目微信小程序项目