💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常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 深度学习实战项目
基于大数据的国内各省高校数据分析可视化系统介绍
基于大数据框架的国内各省高校数据分析可视化系统是一套完整的数据分析解决方案，该系统采用Hadoop分布式存储架构和Spark大数据计算引擎作为核心技术底座，能够高效处理海量高校数据信息。系统整合了全国各省份高校的基础属性、综合实力、办学特色等多维度数据，通过Spark SQL实现快速数据查询与统计分析，配合Echarts图表组件将分析结果以直观的可视化形式呈现。在功能设计上，系统提供了各省高校数据查询、高校属性结构分析、综合实力与特色挖掘、区域与类型交叉分析、空间分布分析以及数据大屏展示等核心模块，用户可以通过前端Vue界面便捷地查看不同维度的高校数据统计图表。系统后端支持Django和Spring Boot两种技术方案，前端采用Vue+ElementUI构建交互界面，数据存储在MySQL数据库中，通过Hadoop HDFS实现大数据文件的分布式存储，整体架构保证了系统在处理大规模数据时的稳定性和响应速度。
基于大数据的国内各省高校数据分析可视化系统演示视频
基于大数据的国内各省高校数据分析可视化系统演示图片
基于大数据的国内各省高校数据分析可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, when, row_number, desc, asc
from pyspark.sql.window import Window
import pandas as pd
from django.http import JsonResponse
spark = SparkSession.builder.appName("UniversityDataAnalysis").config("spark.sql.warehouse.dir", "/user/hive/warehouse").config("spark.executor.memory", "2g").config("spark.driver.memory", "1g").getOrCreate()
def analyze_university_structure(request):
province = request.GET.get('province', None)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/university_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "university_info").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("university_temp")
if province:
structure_sql = f"SELECT university_type, COUNT(*) as type_count, AVG(student_number) as avg_students FROM university_temp WHERE province='{province}' GROUP BY university_type ORDER BY type_count DESC"
else:
structure_sql = "SELECT university_type, COUNT(*) as type_count, AVG(student_number) as avg_students FROM university_temp GROUP BY university_type ORDER BY type_count DESC"
structure_result = spark.sql(structure_sql)
type_distribution = structure_result.collect()
type_data = [{"type": row["university_type"], "count": row["type_count"], "avg_students": int(row["avg_students"])} for row in type_distribution]
if province:
level_sql = f"SELECT school_level, COUNT(*) as level_count FROM university_temp WHERE province='{province}' GROUP BY school_level"
else:
level_sql = "SELECT school_level, COUNT(*) as level_count FROM university_temp GROUP BY school_level"
level_result = spark.sql(level_sql)
level_distribution = level_result.collect()
level_data = [{"level": row["school_level"], "count": row["level_count"]} for row in level_distribution]
if province:
nature_sql = f"SELECT school_nature, COUNT(*) as nature_count FROM university_temp WHERE province='{province}' GROUP BY school_nature"
else:
nature_sql = "SELECT school_nature, COUNT(*) as nature_count FROM university_temp GROUP BY school_nature"
nature_result = spark.sql(nature_sql)
nature_distribution = nature_result.collect()
nature_data = [{"nature": row["school_nature"], "count": row["nature_count"]} for row in nature_distribution]
return JsonResponse({"status": "success", "type_data": type_data, "level_data": level_data, "nature_data": nature_data})
def analyze_comprehensive_strength(request):
province_filter = request.GET.get('province', None)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/university_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "university_info").option("user", "root").option("password", "123456").load()
df = df.withColumn("comprehensive_score", col("academic_score") * 0.4 + col("faculty_score") * 0.3 + col("facility_score") * 0.2 + col("reputation_score") * 0.1)
df.createOrReplaceTempView("university_score_temp")
if province_filter:
top_sql = f"SELECT university_name, province, comprehensive_score, academic_score, faculty_score, key_disciplines FROM university_score_temp WHERE province='{province_filter}' ORDER BY comprehensive_score DESC LIMIT 20"
else:
top_sql = "SELECT university_name, province, comprehensive_score, academic_score, faculty_score, key_disciplines FROM university_score_temp ORDER BY comprehensive_score DESC LIMIT 20"
top_universities = spark.sql(top_sql)
top_result = top_universities.collect()
top_data = [{"name": row["university_name"], "province": row["province"], "score": float(row["comprehensive_score"]), "academic": float(row["academic_score"]), "faculty": float(row["faculty_score"]), "disciplines": row["key_disciplines"]} for row in top_result]
windowSpec = Window.partitionBy("province").orderBy(desc("comprehensive_score"))
df_with_rank = df.withColumn("province_rank", row_number().over(windowSpec))
df_with_rank.createOrReplaceTempView("university_rank_temp")
province_top_sql = "SELECT province, university_name, comprehensive_score, province_rank FROM university_rank_temp WHERE province_rank <= 5"
province_top_result = spark.sql(province_top_sql)
province_top_data = province_top_result.collect()
province_ranking = {}
for row in province_top_data:
prov = row["province"]
if prov not in province_ranking:
province_ranking[prov] = []
province_ranking[prov].append({"name": row["university_name"], "score": float(row["comprehensive_score"]), "rank": row["province_rank"]})
feature_sql = "SELECT university_name, key_disciplines, specialty_major, research_output FROM university_score_temp WHERE key_disciplines IS NOT NULL ORDER BY comprehensive_score DESC LIMIT 30"
feature_result = spark.sql(feature_sql)
feature_data = feature_result.collect()
feature_list = [{"name": row["university_name"], "disciplines": row["key_disciplines"], "major": row["specialty_major"], "research": row["research_output"]} for row in feature_data]
return JsonResponse({"status": "success", "top_universities": top_data, "province_ranking": province_ranking, "feature_universities": feature_list})
def analyze_region_type_cross(request):
region_param = request.GET.get('region', None)
type_param = request.GET.get('type', None)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/university_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "university_info").option("user", "root").option("password", "123456").load()
df = df.withColumn("region", when(col("province").isin(["北京", "天津", "河北", "山西", "内蒙古"]), "华北").when(col("province").isin(["辽宁", "吉林", "黑龙江"]), "东北").when(col("province").isin(["上海", "江苏", "浙江", "安徽", "福建", "江西", "山东"]), "华东").when(col("province").isin(["河南", "湖北", "湖南"]), "华中").when(col("province").isin(["广东", "广西", "海南"]), "华南").when(col("province").isin(["重庆", "四川", "贵州", "云南", "西藏"]), "西南").otherwise("西北"))
df.createOrReplaceTempView("university_region_temp")
if region_param and type_param:
cross_sql = f"SELECT region, university_type, COUNT(*) as count, AVG(student_number) as avg_students, SUM(faculty_number) as total_faculty FROM university_region_temp WHERE region='{region_param}' AND university_type='{type_param}' GROUP BY region, university_type"
elif region_param:
cross_sql = f"SELECT region, university_type, COUNT(*) as count, AVG(student_number) as avg_students, SUM(faculty_number) as total_faculty FROM university_region_temp WHERE region='{region_param}' GROUP BY region, university_type ORDER BY count DESC"
elif type_param:
cross_sql = f"SELECT region, university_type, COUNT(*) as count, AVG(student_number) as avg_students, SUM(faculty_number) as total_faculty FROM university_region_temp WHERE university_type='{type_param}' GROUP BY region, university_type ORDER BY count DESC"
else:
cross_sql = "SELECT region, university_type, COUNT(*) as count, AVG(student_number) as avg_students, SUM(faculty_number) as total_faculty FROM university_region_temp GROUP BY region, university_type ORDER BY region, count DESC"
cross_result = spark.sql(cross_sql)
cross_data = cross_result.collect()
cross_analysis = [{"region": row["region"], "type": row["university_type"], "count": row["count"], "avg_students": int(row["avg_students"]), "total_faculty": int(row["total_faculty"])} for row in cross_data]
region_summary_sql = "SELECT region, COUNT(*) as total_count, AVG(student_number) as region_avg_students FROM university_region_temp GROUP BY region ORDER BY total_count DESC"
region_summary = spark.sql(region_summary_sql)
region_summary_data = region_summary.collect()
region_stats = [{"region": row["region"], "total": row["total_count"], "avg_students": int(row["region_avg_students"])} for row in region_summary_data]
type_summary_sql = "SELECT university_type, COUNT(*) as type_total, AVG(student_number) as type_avg_students FROM university_region_temp GROUP BY university_type ORDER BY type_total DESC"
type_summary = spark.sql(type_summary_sql)
type_summary_data = type_summary.collect()
type_stats = [{"type": row["university_type"], "total": row["type_total"], "avg_students": int(row["type_avg_students"])} for row in type_summary_data]
return JsonResponse({"status": "success", "cross_analysis": cross_analysis, "region_stats": region_stats, "type_stats": type_stats})
基于大数据的国内各省高校数据分析可视化系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常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 深度学习实战项目
