💖💖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
基于大数据的国家公务员招录职位信息可视化分析系统介绍
基于大数据的国家公务员招录职位信息可视化分析系统是一套完整的数据处理与展示平台，采用Hadoop+Spark大数据框架作为核心技术架构，实现对海量公务员招录数据的高效处理与深度挖掘。系统后端提供Python+Django和Java+SpringBoot两种技术实现方案，前端采用Vue+ElementUI构建交互界面，结合Echarts图表库完成数据可视化呈现。在数据处理层面，系统利用HDFS分布式文件系统存储原始招录数据，通过Spark SQL进行结构化查询，配合Pandas和NumPy完成数据清洗与统计分析。功能模块涵盖系统首页、招录数据管理、竞争格局分析、多维交叉分析、宏观态势分析、职位特征分析以及可视化大屏展示七大板块，能够从不同维度呈现公务员招录的职位分布、专业需求、学历要求、地域差异等关键信息，为用户提供直观的数据洞察和决策支持，整个系统充分体现了大数据技术在公共数据分析领域的应用价值。
基于大数据的国家公务员招录职位信息可视化分析系统演示视频
基于大数据的国家公务员招录职位信息可视化分析系统演示图片
基于大数据的国家公务员招录职位信息可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, desc, when, sum as spark_sum, round as spark_round
from django.http import JsonResponse
from django.views import View
import json
spark = SparkSession.builder.appName("PublicServantRecruitmentAnalysis").config("spark.sql.shuffle.partitions", "4").getOrCreate()
class CompetitionAnalysisView(View):
def get(self, request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/recruitment_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "recruitment_positions").option("user", "root").option("password", "root").load()
df.createOrReplaceTempView("positions")
competition_data = spark.sql("""
SELECT department_name, position_name, recruit_number,
applicant_count, pass_review_count,
ROUND(applicant_count / recruit_number, 2) as competition_ratio,
ROUND(pass_review_count / applicant_count * 100, 2) as pass_rate
FROM positions
WHERE recruit_number > 0 AND applicant_count > 0
ORDER BY competition_ratio DESC
LIMIT 100
""")
result_list = competition_data.collect()
department_stats = df.groupBy("department_name").agg(
count("position_id").alias("position_count"),
spark_sum("recruit_number").alias("total_recruit"),
spark_sum("applicant_count").alias("total_applicant"),
spark_round(avg(col("applicant_count") / col("recruit_number")), 2).alias("avg_competition")
).orderBy(desc("avg_competition"))
dept_result = department_stats.collect()
hot_positions = df.filter(col("applicant_count") > 1000).select(
"department_name", "position_name", "recruit_number",
"applicant_count", "education_requirement", "major_requirement"
).orderBy(desc("applicant_count")).limit(50)
hot_list = hot_positions.collect()
zero_competition = df.filter(col("applicant_count") == 0).select(
"department_name", "position_name", "education_requirement",
"work_location", "political_status"
).collect()
response_data = {
"competition_ranking": [row.asDict() for row in result_list],
"department_statistics": [row.asDict() for row in dept_result],
"hot_positions": [row.asDict() for row in hot_list],
"zero_applicant_positions": [row.asDict() for row in zero_competition],
"total_analyzed": df.count()
}
return JsonResponse(response_data, safe=False)
class MultiDimensionalAnalysisView(View):
def post(self, request):
params = json.loads(request.body)
education = params.get('education', None)
major = params.get('major', None)
location = params.get('location', None)
department = params.get('department', None)
min_recruit = params.get('min_recruit', 0)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/recruitment_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "recruitment_positions").option("user", "root").option("password", "root").load()
filtered_df = df
if education:
filtered_df = filtered_df.filter(col("education_requirement") == education)
if major:
filtered_df = filtered_df.filter(col("major_requirement").like(f"%{major}%"))
if location:
filtered_df = filtered_df.filter(col("work_location").like(f"%{location}%"))
if department:
filtered_df = filtered_df.filter(col("department_name").like(f"%{department}%"))
if min_recruit > 0:
filtered_df = filtered_df.filter(col("recruit_number") >= min_recruit)
education_distribution = filtered_df.groupBy("education_requirement").agg(
count("position_id").alias("position_count"),
spark_sum("recruit_number").alias("recruit_sum")
).orderBy(desc("position_count"))
major_distribution = filtered_df.groupBy("major_requirement").agg(
count("position_id").alias("count")
).orderBy(desc("count")).limit(20)
location_distribution = filtered_df.groupBy("work_location").agg(
count("position_id").alias("position_count"),
spark_sum("recruit_number").alias("total_recruit")
).orderBy(desc("position_count"))
political_stats = filtered_df.groupBy("political_status").agg(
count("position_id").alias("count")
).orderBy(desc("count"))
work_experience_stats = filtered_df.groupBy("work_experience").agg(
count("position_id").alias("count")
).orderBy(desc("count"))
base_layer_stats = filtered_df.groupBy("base_work_experience").agg(
count("position_id").alias("count")
).orderBy(desc("count"))
cross_analysis = filtered_df.groupBy("education_requirement", "work_location").agg(
count("position_id").alias("position_count"),
spark_round(avg("recruit_number"), 2).alias("avg_recruit")
).orderBy(desc("position_count"))
result_data = {
"total_matched": filtered_df.count(),
"education_dist": [row.asDict() for row in education_distribution.collect()],
"major_dist": [row.asDict() for row in major_distribution.collect()],
"location_dist": [row.asDict() for row in location_distribution.collect()],
"political_dist": [row.asDict() for row in political_stats.collect()],
"experience_dist": [row.asDict() for row in work_experience_stats.collect()],
"base_layer_dist": [row.asDict() for row in base_layer_stats.collect()],
"cross_analysis": [row.asDict() for row in cross_analysis.collect()],
"filter_conditions": params
}
return JsonResponse(result_data, safe=False)
class MacroTrendAnalysisView(View):
def get(self, request):
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/recruitment_db").option("driver", "com.mysql.cj.jdbc.Driver").option("dbtable", "recruitment_positions").option("user", "root").option("password", "root").load()
df.createOrReplaceTempView("positions")
overall_stats = spark.sql("""
SELECT
COUNT(DISTINCT department_name) as total_departments,
COUNT(position_id) as total_positions,
SUM(recruit_number) as total_recruits,
SUM(applicant_count) as total_applicants,
ROUND(AVG(applicant_count / recruit_number), 2) as avg_competition_ratio
FROM positions
WHERE recruit_number > 0
""")
education_trend = df.groupBy("education_requirement").agg(
count("position_id").alias("position_count"),
spark_sum("recruit_number").alias("recruit_count"),
spark_round((count("position_id") / df.count() * 100), 2).alias("percentage")
).orderBy(desc("position_count"))
region_distribution = df.groupBy("work_location").agg(
count("position_id").alias("position_count"),
spark_sum("recruit_number").alias("recruit_count"),
spark_sum("applicant_count").alias("applicant_count")
).orderBy(desc("recruit_count")).limit(30)
department_scale = df.groupBy("department_name").agg(
count("position_id").alias("position_count"),
spark_sum("recruit_number").alias("recruit_count")
).orderBy(desc("recruit_count")).limit(20)
major_demand = df.filter(col("major_requirement") != "不限").groupBy("major_requirement").agg(
count("position_id").alias("demand_count")
).orderBy(desc("demand_count")).limit(30)
political_requirement = df.groupBy("political_status").agg(
count("position_id").alias("count"),
spark_round((count("position_id") / df.count() * 100), 2).alias("percentage")
).orderBy(desc("count"))
experience_requirement = df.groupBy("work_experience").agg(
count("position_id").alias("count"),
spark_round((count("position_id") / df.count() * 100), 2).alias("percentage")
).orderBy(desc("count"))
base_layer_requirement = df.groupBy("base_work_experience").agg(
count("position_id").alias("count"),
spark_round((count("position_id") / df.count() * 100), 2).alias("percentage")
).orderBy(desc("count"))
recruit_scale_distribution = df.withColumn(
"recruit_scale",
when(col("recruit_number") == 1, "1人")
.when((col("recruit_number") >= 2) & (col("recruit_number") <= 5), "2-5人")
.when((col("recruit_number") >= 6) & (col("recruit_number") <= 10), "6-10人")
.otherwise("10人以上")
).groupBy("recruit_scale").agg(
count("position_id").alias("count")
).orderBy(desc("count"))
macro_data = {
"overall_statistics": overall_stats.collect()[0].asDict(),
"education_trend": [row.asDict() for row in education_trend.collect()],
"region_distribution": [row.asDict() for row in region_distribution.collect()],
"department_scale": [row.asDict() for row in department_scale.collect()],
"major_demand_top": [row.asDict() for row in major_demand.collect()],
"political_requirement": [row.asDict() for row in political_requirement.collect()],
"experience_requirement": [row.asDict() for row in experience_requirement.collect()],
"base_layer_requirement": [row.asDict() for row in base_layer_requirement.collect()],
"recruit_scale_dist": [row.asDict() for row in recruit_scale_distribution.collect()]
}
return JsonResponse(macro_data, safe=False)
基于大数据的国家公务员招录职位信息可视化分析系统文档展示
💖💖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
