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【数据分析】基于大数据的BOSS直聘就业岗位数据可视化分析系统 | 毕业设计实战任务 选题推荐 可视化大屏 Hadoop SPark java Python

2025-10-11 19:37  tlnshuju  阅读(24)  评论(0)    收藏  举报

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，研发项目包括大材料、深度学习、网站、小软件、安卓、算法。平常会做一些计划定制化制作、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挑战的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！
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目录

基于大数据的BOSS直聘就业岗位资料可视化分析系统介绍

基于大数据的BOSS直聘就业岗位素材可视化分析系统是一个综合运用Hadoop分布式存储和Spark大数据处理技巧的数据分析平台，专门针对BOSS直聘平台的岗位信息进行深度挖掘和可视化展示。系统采用Python作为关键开发语言，结合Django框架构建稳定的后端服务架构，前端使用Vue+ElementUI+Echarts技术栈实现用户友好的交互界面和丰富的图表展示效果。系统通过Hadoop HDFS存储海量岗位资料，利用Spark SQL进行高效的素材查询和分析处理，结合Pandas和NumPy进行数据清洗和统计计算。核心功能模块包括薪酬水平分析、市场需求分析、技能福利分析和多维交叉分析，能够从不同维度对岗位数据进行深入剖析，为用户提供直观的材料可视化结果。系统还配备了大屏可视化模块，承受实时数据展示和动态图表更新，同时具备完善的用户管理效果，包括个人信息维护和密码管理等基础服务，整体架构设计合理，技术选型恰当，能够高效处理大规模岗位内容的存储、分析和展示需求。

基于大数据的BOSS直聘就业岗位数据可视化分析系统演示视频

【数据分析】基于大数据的BOSS直聘就业岗位数据可视化分析系统 | 毕业设计实战工程 选题推荐 可视化大屏 Hadoop SPark java Python

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基于大内容的BOSS直聘就业岗位数据可视化分析系统代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, desc, asc, sum as spark_sum, max as spark_max, min as spark_min
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
from datetime import datetime
spark = SparkSession.builder.appName("BOSSDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def salary_analysis(request):
try:
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/boss_data").option("dbtable", "job_positions").option("user", "root").option("password", "password").load()
df.createOrReplaceTempView("positions")
salary_stats = spark.sql("SELECT industry, city, AVG(salary_min) as avg_min_salary, AVG(salary_max) as avg_max_salary, COUNT(*) as position_count FROM positions WHERE salary_min > 0 AND salary_max > 0 GROUP BY industry, city ORDER BY avg_max_salary DESC")
salary_range_analysis = spark.sql("SELECT CASE WHEN salary_max <= 8000 THEN '0-8K' WHEN salary_max <= 15000 THEN '8-15K' WHEN salary_max <= 25000 THEN '15-25K' WHEN salary_max <= 40000 THEN '25-40K' ELSE '40K+' END as salary_range, COUNT(*) as count, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM positions WHERE salary_max > 0), 2) as percentage FROM positions WHERE salary_max > 0 GROUP BY salary_range ORDER BY salary_max")
  experience_salary = spark.sql("SELECT experience_required, AVG(salary_max) as avg_salary, COUNT(*) as job_count FROM positions WHERE salary_max > 0 AND experience_required IS NOT NULL GROUP BY experience_required ORDER BY avg_salary DESC")
  education_salary = spark.sql("SELECT education_required, AVG(salary_min) as min_avg, AVG(salary_max) as max_avg, COUNT(*) as count FROM positions WHERE salary_max > 0 AND education_required IS NOT NULL GROUP BY education_required ORDER BY max_avg DESC")
  monthly_trend = spark.sql("SELECT DATE_FORMAT(publish_time, 'yyyy-MM') as month, AVG(salary_max) as avg_salary, COUNT(*) as job_count FROM positions WHERE salary_max > 0 AND publish_time >= date_sub(current_date(), 365) GROUP BY month ORDER BY month")
  company_size_salary = spark.sql("SELECT company_size, AVG(salary_min) as avg_min, AVG(salary_max) as avg_max, COUNT(*) as position_count FROM positions WHERE salary_max > 0 AND company_size IS NOT NULL GROUP BY company_size ORDER BY avg_max DESC")
  result_data = {
  'salary_stats': [row.asDict() for row in salary_stats.collect()],
  'salary_distribution': [row.asDict() for row in salary_range_analysis.collect()],
  'experience_analysis': [row.asDict() for row in experience_salary.collect()],
  'education_analysis': [row.asDict() for row in education_salary.collect()],
  'trend_analysis': [row.asDict() for row in monthly_trend.collect()],
  'company_analysis': [row.asDict() for row in company_size_salary.collect()]
  }
  return JsonResponse({'status': 'success', 'data': result_data})
  except Exception as e:
  return JsonResponse({'status': 'error', 'message': str(e)})
  def market_demand_analysis(request):
  try:
  df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/boss_data").option("dbtable", "job_positions").option("user", "root").option("password", "password").load()
  df.createOrReplaceTempView("positions")
  industry_demand = spark.sql("SELECT industry, COUNT(*) as job_count, COUNT(DISTINCT company_name) as company_count, AVG(salary_max) as avg_salary FROM positions WHERE publish_time >= date_sub(current_date(), 30) GROUP BY industry ORDER BY job_count DESC LIMIT 20")
  city_demand = spark.sql("SELECT city, COUNT(*) as position_count, COUNT(DISTINCT industry) as industry_diversity, AVG(salary_max) as avg_salary FROM positions WHERE publish_time >= date_sub(current_date(), 30) GROUP BY city ORDER BY position_count DESC LIMIT 15")
  skill_demand = spark.sql("SELECT skill_tags, COUNT(*) as demand_count, AVG(salary_max) as avg_salary FROM positions WHERE skill_tags IS NOT NULL AND skill_tags != '' AND publish_time >= date_sub(current_date(), 30) GROUP BY skill_tags ORDER BY demand_count DESC LIMIT 30")
  position_demand = spark.sql("SELECT position_name, COUNT(*) as job_count, AVG(salary_min) as avg_min_salary, AVG(salary_max) as avg_max_salary, COUNT(DISTINCT city) as city_spread FROM positions WHERE publish_time >= date_sub(current_date(), 30) GROUP BY position_name ORDER BY job_count DESC LIMIT 25")
  growth_trend = spark.sql("SELECT DATE_FORMAT(publish_time, 'yyyy-MM-dd') as date, COUNT(*) as daily_jobs, COUNT(DISTINCT company_name) as active_companies FROM positions WHERE publish_time >= date_sub(current_date(), 90) GROUP BY date ORDER BY date")
  urgent_positions = spark.sql("SELECT industry, position_name, city, COUNT(*) as urgent_count, AVG(salary_max) as salary FROM positions WHERE urgent_flag = 1 AND publish_time >= date_sub(current_date(), 7) GROUP BY industry, position_name, city ORDER BY urgent_count DESC LIMIT 20")
  company_activity = spark.sql("SELECT company_name, company_size, COUNT(*) as job_posted, COUNT(DISTINCT position_name) as position_variety, AVG(salary_max) as avg_offer FROM positions WHERE publish_time >= date_sub(current_date(), 30) GROUP BY company_name, company_size ORDER BY job_posted DESC LIMIT 30")
  demand_data = {
  'industry_ranking': [row.asDict() for row in industry_demand.collect()],
  'city_ranking': [row.asDict() for row in city_demand.collect()],
  'skill_ranking': [row.asDict() for row in skill_demand.collect()],
  'position_ranking': [row.asDict() for row in position_demand.collect()],
  'growth_trend': [row.asDict() for row in growth_trend.collect()],
  'urgent_analysis': [row.asDict() for row in urgent_positions.collect()],
  'company_activity': [row.asDict() for row in company_activity.collect()]
  }
  return JsonResponse({'status': 'success', 'data': demand_data})
  except Exception as e:
  return JsonResponse({'status': 'error', 'message': str(e)})
  def multi_dimensional_analysis(request):
  try:
  df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/boss_data").option("dbtable", "job_positions").option("user", "root").option("password", "password").load()
  df.createOrReplaceTempView("positions")
  request_data = json.loads(request.body) if request.method == 'POST' else {}
  selected_city = request_data.get('city', None)
  selected_industry = request_data.get('industry', None)
  selected_experience = request_data.get('experience', None)
  base_query = "SELECT * FROM positions WHERE salary_max > 0"
  if selected_city:
  base_query += f" AND city = '{selected_city}'"
  if selected_industry:
  base_query += f" AND industry = '{selected_industry}'"
  if selected_experience:
  base_query += f" AND experience_required = '{selected_experience}'"
  spark.sql(f"CREATE OR REPLACE TEMPORARY VIEW filtered_positions AS {base_query}")
  cross_analysis = spark.sql("SELECT industry, city, experience_required, education_required, AVG(salary_min) as avg_min, AVG(salary_max) as avg_max, COUNT(*) as job_count, COUNT(DISTINCT company_name) as company_count FROM filtered_positions GROUP BY industry, city, experience_required, education_required ORDER BY job_count DESC")
  correlation_analysis = spark.sql("SELECT company_size, industry, AVG(salary_max) as avg_salary, COUNT(*) as position_count, ROUND(AVG(salary_max) / (SELECT AVG(salary_max) FROM filtered_positions) * 100, 2) as salary_index FROM filtered_positions WHERE company_size IS NOT NULL GROUP BY company_size, industry ORDER BY avg_salary DESC")
  skill_salary_matrix = spark.sql("SELECT skill_tags, experience_required, COUNT(*) as job_count, AVG(salary_max) as avg_salary, MIN(salary_max) as min_salary, MAX(salary_max) as max_salary FROM filtered_positions WHERE skill_tags IS NOT NULL AND experience_required IS NOT NULL GROUP BY skill_tags, experience_required ORDER BY avg_salary DESC")
  geographic_distribution = spark.sql("SELECT city, industry, COUNT(*) as job_density, AVG(salary_max) as avg_salary, ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM filtered_positions), 2) as market_share FROM filtered_positions GROUP BY city, industry ORDER BY job_density DESC")
  time_dimension = spark.sql("SELECT DATE_FORMAT(publish_time, 'yyyy-MM') as month, industry, city, COUNT(*) as monthly_jobs, AVG(salary_max) as monthly_avg_salary FROM filtered_positions WHERE publish_time >= date_sub(current_date(), 180) GROUP BY month, industry, city ORDER BY month, monthly_jobs DESC")
  competitive_analysis = spark.sql("SELECT position_name, city, COUNT(*) as competition_level, AVG(salary_max) as market_price, STDDEV(salary_max) as salary_variance FROM filtered_positions GROUP BY position_name, city HAVING COUNT(*) >= 5 ORDER BY competition_level DESC")
  comprehensive_ranking = spark.sql("SELECT industry, city, COUNT(*) as total_jobs, AVG(salary_max) as avg_salary, COUNT(DISTINCT company_name) as company_diversity, COUNT(DISTINCT position_name) as position_variety, (COUNT(*) * 0.4 + AVG(salary_max)/1000 * 0.3 + COUNT(DISTINCT company_name) * 0.3) as comprehensive_score FROM filtered_positions GROUP BY industry, city ORDER BY comprehensive_score DESC LIMIT 20")
  analysis_result = {
  'cross_analysis': [row.asDict() for row in cross_analysis.collect()],
  'correlation_data': [row.asDict() for row in correlation_analysis.collect()],
  'skill_matrix': [row.asDict() for row in skill_salary_matrix.collect()],
  'geographic_data': [row.asDict() for row in geographic_distribution.collect()],
  'time_series': [row.asDict() for row in time_dimension.collect()],
  'competition_analysis': [row.asDict() for row in competitive_analysis.collect()],
  'ranking_data': [row.asDict() for row in comprehensive_ranking.collect()]
  }
  return JsonResponse({'status': 'success', 'data': analysis_result, 'filters': request_data})
  except Exception as e:
  return JsonResponse({'status': 'error', 'message': str(e)})

基于大内容的BOSS直聘就业岗位内容可视化分析系统文档展示

在这里插入图片描述

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、微信小应用、Python、Golang、安卓Android等，开发项目包括大素材、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研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难题允许问我！
想说的话：感谢大家的关注与支持！

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