一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
大数据框架：Hadoop+Spark（Hive需要定制修改） 开发语言：Java+Python（两个版本都支持） 数据库：MySQL 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持） 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery
国家公务员招录职位信息可视化分析系统是一个基于大数据技术构建的智能化分析平台，专门针对公务员招录数据进行深度挖掘和可视化展示。系统采用Hadoop+Spark大数据框架作为底层数据处理引擎，结合Django后端框架和Vue+ElementUI+Echarts前端技术栈，构建了完整的数据分析生态。通过HDFS分布式文件系统存储海量招录数据，利用Spark SQL进行高效的数据查询和计算，配合Pandas、NumPy等数据科学库实现复杂的统计分析。系统提供用户管理、招录数据管理、竞争格局分析、多维交叉分析、宏观态势分析、职位特征分析等核心功能模块，并通过可视化大屏实时展示分析结果。平台能够处理不同年度、不同部门、不同地区的招录数据，为考生择业决策、政府人才规划、学术研究等提供数据支撑，实现了从数据采集、存储、处理到可视化展示的全流程自动化管理。
三、视频解说
四、部分功能展示
五、部分代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("GovJobAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def competition_analysis(request):
job_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/govjob").option("dbtable", "recruitment_positions").option("user", "root").option("password", "password").load()
competition_df = job_data.groupBy("department", "position_name", "recruitment_number").agg(count("application_id").alias("applicant_count")).withColumn("competition_ratio", col("applicant_count") / col("recruitment_number"))
high_competition = competition_df.filter(col("competition_ratio") > 100).orderBy(desc("competition_ratio"))
dept_competition = competition_df.groupBy("department").agg(avg("competition_ratio").alias("avg_ratio"), max("competition_ratio").alias("max_ratio"), min("competition_ratio").alias("min_ratio"))
trend_data = job_data.withColumn("apply_date", to_date(col("application_time"))).groupBy("apply_date").agg(count("application_id").alias("daily_applications")).orderBy("apply_date")
window_spec = Window.orderBy("apply_date").rowsBetween(-6, 0)
trend_with_ma = trend_data.withColumn("moving_avg", avg("daily_applications").over(window_spec))
result_data = {"high_competition_positions": high_competition.limit(20).toPandas().to_dict("records"), "department_statistics": dept_competition.toPandas().to_dict("records"), "application_trend": trend_with_ma.toPandas().to_dict("records")}
correlation_matrix = job_data.select("education_requirement", "work_experience", "competition_ratio").toPandas().corr()
result_data["correlation_analysis"] = correlation_matrix.to_dict()
return JsonResponse(result_data, safe=False)
def multidimensional_analysis(request):
params = json.loads(request.body)
selected_dimensions = params.get('dimensions', ['department', 'education', 'location'])
job_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/govjob").option("dbtable", "recruitment_positions").option("user", "root").option("password", "password").load()
cross_analysis = job_data.groupBy(*selected_dimensions).agg(count("position_id").alias("position_count"), sum("recruitment_number").alias("total_recruitment"), avg("salary_min").alias("avg_min_salary"), avg("salary_max").alias("avg_max_salary"))
pivot_education = job_data.groupBy("department").pivot("education_requirement").agg(count("position_id")).fillna(0)
pivot_location = job_data.groupBy("location").pivot("position_level").agg(sum("recruitment_number")).fillna(0)
education_dist = job_data.groupBy("education_requirement").agg(count("position_id").alias("count")).withColumn("percentage", col("count") * 100.0 / sum("count").over(Window.partitionBy()))
salary_ranges = job_data.withColumn("salary_range", when(col("salary_max") < 5000, "低薪").when(col("salary_max") < 8000, "中薪").otherwise("高薪")).groupBy("salary_range", "department").agg(count("position_id").alias("count"))
geographic_analysis = job_data.groupBy("province", "city").agg(count("position_id").alias("position_count"), avg("competition_ratio").alias("avg_competition")).orderBy(desc("position_count"))
skills_requirement = job_data.select("position_id", "required_skills").rdd.flatMap(lambda row: [(skill.strip(), 1) for skill in row.required_skills.split(",") if skill.strip()]).reduceByKey(lambda a, b: a + b).toDF(["skill", "frequency"]).orderBy(desc("frequency"))
result = {"cross_analysis": cross_analysis.toPandas().to_dict("records"), "education_pivot": pivot_education.toPandas().to_dict("records"), "location_pivot": pivot_location.toPandas().to_dict("records"), "education_distribution": education_dist.toPandas().to_dict("records"), "salary_analysis": salary_ranges.toPandas().to_dict("records"), "geographic_stats": geographic_analysis.limit(50).toPandas().to_dict("records"), "skills_ranking": skills_requirement.limit(30).toPandas().to_dict("records")}
return JsonResponse(result, safe=False)
def macro_trend_analysis(request):
params = json.loads(request.body)
year_range = params.get('years', [2020, 2021, 2022, 2023, 2024])
job_data = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/govjob").option("dbtable", "recruitment_positions").option("user", "root").option("password", "password").load()
yearly_trend = job_data.filter(col("recruitment_year").isin(year_range)).groupBy("recruitment_year").agg(count("position_id").alias("total_positions"), sum("recruitment_number").alias("total_recruitment"), countDistinct("department").alias("department_count"), avg("competition_ratio").alias("avg_competition"))
department_growth = job_data.filter(col("recruitment_year").isin(year_range)).groupBy("department", "recruitment_year").agg(sum("recruitment_number").alias("yearly_recruitment")).orderBy("department", "recruitment_year")
window_dept = Window.partitionBy("department").orderBy("recruitment_year")
dept_with_growth = department_growth.withColumn("prev_year_recruitment", lag("yearly_recruitment").over(window_dept)).withColumn("growth_rate", (col("yearly_recruitment") - col("prev_year_recruitment")) / col("prev_year_recruitment") * 100)
policy_impact = job_data.join(spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/govjob").option("dbtable", "policy_events").option("user", "root").option("password", "password").load(), "recruitment_year", "left").groupBy("policy_category", "recruitment_year").agg(avg("recruitment_number").alias("avg_recruitment_after_policy"))
seasonal_pattern = job_data.withColumn("month", month(col("publication_date"))).groupBy("month").agg(count("position_id").alias("monthly_positions"), avg("competition_ratio").alias("monthly_competition")).orderBy("month")
regional_development = job_data.groupBy("economic_zone", "recruitment_year").agg(sum("recruitment_number").alias("zone_recruitment"), avg("salary_max").alias("avg_salary")).orderBy("economic_zone", "recruitment_year")
forecast_data = yearly_trend.orderBy("recruitment_year").toPandas()
if len(forecast_data) >= 3:
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(forecast_data['recruitment_year'], forecast_data['total_recruitment'])
next_year_forecast = slope * (max(year_range) + 1) + intercept
forecast_data['forecast_next_year'] = next_year_forecast
result = {"yearly_overview": yearly_trend.orderBy("recruitment_year").toPandas().to_dict("records"), "department_growth_trend": dept_with_growth.filter(col("growth_rate").isNotNull()).toPandas().to_dict("records"), "policy_impact_analysis": policy_impact.toPandas().to_dict("records"), "seasonal_patterns": seasonal_pattern.toPandas().to_dict("records"), "regional_development": regional_development.toPandas().to_dict("records"), "forecast_analysis": forecast_data.to_dict("records") if len(forecast_data) >= 3 else []}
return JsonResponse(result, safe=False)
六、部分文档展示
七、END
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