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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。
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文章目录
一、前言
本系统名为《基于大数据的广告投放数据可视化分析系统》，主要是把广告投放过程中产生的各类数据用大数据技术跑一遍，再用图表和大屏的方式展示出来，让使用者能比较直观地看到广告投放的整体情况。技术这块用的是Hadoop加Spark这套大数据框架，底层靠HDFS做数据存储，计算和分析主要交给Spark和Spark SQL，中间也会用到Pandas和NumPy做数据处理和数值计算，后端提供Python的Django版本和Java的SpringBoot版本两套实现，前端用Vue配合ElementUI、Echarts以及HTML、CSS、JavaScript、jQuery来搭页面和画图，数据库用的是MySQL。功能上包含系统首页、大屏可视化、用户管理、广告信息管理、总览分析、渠道分析、时机分析、受众分析、地域分析、机会分析、个人信息和修改密码这些模块。用户登录进去之后，可以在首页看到整体情况，也可以切到大屏可视化页面看核心指标，广告信息模块用来维护广告基础数据，总览分析看整体投放效果，渠道分析对比不同渠道的表现，时机分析看不同时间段的投放差异，受众分析看人群特征，地域分析看不同地区的分布，机会分析则尝试从数据里找出还有提升空间的方向。整个系统说白了就是拿一个广告投放的场景，把大数据分析从存储、计算到可视化这条链路完整走一遍，适合作为计算机专业毕设来练手。
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
- 基于大数据的广告投放数据可视化分析系统系统界面展示：
四、部分代码设计
- 项目实战-代码参考：
python
# 总览分析核心业务处理：从HDFS读取广告投放数据，用Spark SQL做整体指标汇总
def overview_analysis(request):
spark = SparkSession.builder.appName("AdOverviewAnalysis").master("local[*]").config("spark.sql.warehouse.dir", "/user/hive/warehouse").enableHiveSupport().getOrCreate()
ad_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/ad_data/ad_put.csv")
ad_df.createOrReplaceTempView("ad_put")
total_df = spark.sql("SELECT COUNT(*) AS total_count, SUM(impression) AS total_impression, SUM(click) AS total_click, SUM(convert) AS total_convert, SUM(cost) AS total_cost FROM ad_put")
total_row = total_df.collect()[0]
total_count = int(total_row["total_count"]) if total_row["total_count"] is not None else 0
total_impression = int(total_row["total_impression"]) if total_row["total_impression"] is not None else 0
total_click = int(total_row["total_click"]) if total_row["total_click"] is not None else 0
total_convert = int(total_row["total_convert"]) if total_row["total_convert"] is not None else 0
total_cost = float(total_row["total_cost"]) if total_row["total_cost"] is not None else 0.0
ctr = round(total_click / total_impression, 4) if total_impression > 0 else 0.0
cvr = round(total_convert / total_click, 4) if total_click > 0 else 0.0
cpc = round(total_cost / total_click, 2) if total_click > 0 else 0.0
cpm = round(total_cost / total_impression * 1000, 2) if total_impression > 0 else 0.0
result = {"total_count": total_count, "total_impression": total_impression, "total_click": total_click, "total_convert": total_convert, "total_cost": total_cost, "ctr": ctr, "cvr": cvr, "cpc": cpc, "cpm": cpm}
spark.stop()
return JsonResponse({"code": 200, "msg": "总览分析成功", "data": result})
# 渠道分析核心业务处理：按渠道分组，用Spark SQL算出各渠道的投放效果并排序
def channel_analysis(request):
spark = SparkSession.builder.appName("AdChannelAnalysis").master("local[*]").config("spark.sql.warehouse.dir", "/user/hive/warehouse").enableHiveSupport().getOrCreate()
ad_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/ad_data/ad_put.csv")
ad_df.createOrReplaceTempView("ad_put")
channel_df = spark.sql("SELECT channel, COUNT(*) AS ad_count, SUM(impression) AS impression, SUM(click) AS click, SUM(convert) AS convert, SUM(cost) AS cost FROM ad_put GROUP BY channel ORDER BY cost DESC")
channel_list = []
for row in channel_df.collect():
impression = int(row["impression"]) if row["impression"] is not None else 0
click = int(row["click"]) if row["click"] is not None else 0
convert = int(row["convert"]) if row["convert"] is not None else 0
cost = float(row["cost"]) if row["cost"] is not None else 0.0
ctr = round(click / impression, 4) if impression > 0 else 0.0
cvr = round(convert / click, 4) if click > 0 else 0.0
cpc = round(cost / click, 2) if click > 0 else 0.0
channel_list.append({"channel": row["channel"], "ad_count": int(row["ad_count"]), "impression": impression, "click": click, "convert": convert, "cost": cost, "ctr": ctr, "cvr": cvr, "cpc": cpc})
spark.stop()
return JsonResponse({"code": 200, "msg": "渠道分析成功", "data": channel_list})
# 地域分析核心业务处理：按地域分组，用Spark SQL统计各地域投放情况并计算转化率
def region_analysis(request):
spark = SparkSession.builder.appName("AdRegionAnalysis").master("local[*]").config("spark.sql.warehouse.dir", "/user/hive/warehouse").enableHiveSupport().getOrCreate()
ad_df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/ad_data/ad_put.csv")
ad_df.createOrReplaceTempView("ad_put")
region_df = spark.sql("SELECT region, COUNT(*) AS ad_count, SUM(impression) AS impression, SUM(click) AS click, SUM(convert) AS convert, SUM(cost) AS cost FROM ad_put GROUP BY region ORDER BY convert DESC")
region_list = []
for row in region_df.collect():
impression = int(row["impression"]) if row["impression"] is not None else 0
click = int(row["click"]) if row["click"] is not None else 0
convert = int(row["convert"]) if row["convert"] is not None else 0
cost = float(row["cost"]) if row["cost"] is not None else 0.0
ctr = round(click / impression, 4) if impression > 0 else 0.0
cvr = round(convert / click, 4) if click > 0 else 0.0
cpc = round(cost / click, 2) if click > 0 else 0.0
region_list.append({"region": row["region"], "ad_count": int(row["ad_count"]), "impression": impression, "click": click, "convert": convert, "cost": cost, "ctr": ctr, "cvr": cvr, "cpc": cpc})
spark.stop()
return JsonResponse({"code": 200, "msg": "地域分析成功", "data": region_list})五、论文参考
- 计算机毕业设计选题推荐-基于大数据的广告投放数据可视化分析系统系统-论文参考：
六、系统视频
- 基于大数据的广告投放数据可视化分析系统系统-项目视频：
项目演示视频
结语
计算机毕业设计选题推荐:基于大数据的广告投放数据可视化分析系统|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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