💖💖作者：计算机毕业设计杰瑞
💙💙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
深度学校实战项目
计算机毕业设计选题推荐

基于大数据的大模型岗位数据分析与可视化系统介绍

《基于大数据的大模型岗位数据分析与可视化系统》是一个运用Hadoop分布式存储和Spark大数据处理框架构建的智能化岗位数据分析平台。该系统通过HDFS存储海量的大模型相关岗位信息，利用Spark SQL进行高效的数据清洗和分析处理，结合Pandas和NumPy进行深度的数据挖掘。系统采用Django后端框架提供稳定的API服务，前端使用Vue.js配合ElementUI组件库构建现代化的用户界面，通过Echarts实现丰富的数据可视化效果。核心功能涵盖了岗位数据的全方位管理，包括模型岗位数据的采集与存储、企业对不同技能岗位的偏好分析、实时的市场行情监测、详细的薪酬水平统计分析以及当前最热门技能趋势的深度挖掘。系统通过大数据技术的应用，能够处理大规模的岗位数据集，为用户提供准确的行业洞察和决策支持，帮助求职者和企业更好地了解大模型领域的就业市场动态。

基于大数据的大模型岗位数据分析与可视化系统演示视频

基于大数据的大模型岗位数据分析与可视化系统演示图片

基于大数据的大模型岗位数据分析与可视化系统代码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, avg, count, sum, desc, regexp_extract, split, explode
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType
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("ModelJobAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

@csrf_exempt
def analyze_salary_distribution(request):
    if request.method == 'POST':
        data = json.loads(request.body)
        position_type = data.get('position_type', 'all')
        experience_level = data.get('experience_level', 'all')
        df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/job_data/salary_data.csv")
        if position_type != 'all':
            df = df.filter(col("position_name").contains(position_type))
        if experience_level != 'all':
            df = df.filter(col("experience_requirement") == experience_level)
        salary_stats = df.select(avg(col("salary_min")).alias("avg_min_salary"), avg(col("salary_max")).alias("avg_max_salary"), count("*").alias("total_positions"))
        salary_distribution = df.groupBy("salary_range").agg(count("*").alias("position_count")).orderBy(desc("position_count"))
        city_salary = df.groupBy("city").agg(avg((col("salary_min") + col("salary_max")) / 2).alias("avg_salary")).orderBy(desc("avg_salary"))
        company_size_salary = df.groupBy("company_size").agg(avg((col("salary_min") + col("salary_max")) / 2).alias("avg_salary"), count("*").alias("position_count")).orderBy(desc("avg_salary"))
        stats_result = salary_stats.collect()[0]
        distribution_result = [{"salary_range": row["salary_range"], "count": row["position_count"]} for row in salary_distribution.collect()]
        city_result = [{"city": row["city"], "avg_salary": round(row["avg_salary"], 2)} for row in city_salary.collect()]
        company_result = [{"company_size": row["company_size"], "avg_salary": round(row["avg_salary"], 2), "count": row["position_count"]} for row in company_size_salary.collect()]
        return JsonResponse({"status": "success", "avg_min_salary": round(stats_result["avg_min_salary"], 2), "avg_max_salary": round(stats_result["avg_max_salary"], 2), "total_positions": stats_result["total_positions"], "salary_distribution": distribution_result, "city_salary": city_result, "company_size_salary": company_result})

@csrf_exempt
def analyze_market_trends(request):
    if request.method == 'POST':
        data = json.loads(request.body)
        time_range = data.get('time_range', '30')
        industry_type = data.get('industry_type', 'all')
        df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/job_data/market_data.csv")
        df = df.filter(col("publish_date") >= f"2024-{12-int(time_range)//30}-01")
        if industry_type != 'all':
            df = df.filter(col("industry") == industry_type)
        monthly_trends = df.groupBy("publish_month").agg(count("*").alias("job_count"), avg(col("salary_avg")).alias("avg_salary")).orderBy("publish_month")
        industry_demand = df.groupBy("industry").agg(count("*").alias("demand_count"), avg(col("salary_avg")).alias("industry_avg_salary")).orderBy(desc("demand_count"))
        position_growth = df.groupBy("position_category").agg(count("*").alias("current_count")).orderBy(desc("current_count"))
        company_activity = df.groupBy("company_name").agg(count("*").alias("published_positions"), avg(col("salary_avg")).alias("company_avg_salary")).filter(col("published_positions") >= 3).orderBy(desc("published_positions"))
        regional_market = df.groupBy("region").agg(count("*").alias("market_demand"), avg(col("salary_avg")).alias("regional_salary")).orderBy(desc("market_demand"))
        trends_result = [{"month": row["publish_month"], "job_count": row["job_count"], "avg_salary": round(row["avg_salary"], 2)} for row in monthly_trends.collect()]
        industry_result = [{"industry": row["industry"], "demand": row["demand_count"], "avg_salary": round(row["industry_avg_salary"], 2)} for row in industry_demand.collect()]
        growth_result = [{"category": row["position_category"], "count": row["current_count"]} for row in position_growth.collect()]
        company_result = [{"company": row["company_name"], "positions": row["published_positions"], "avg_salary": round(row["company_avg_salary"], 2)} for row in company_activity.collect()]
        regional_result = [{"region": row["region"], "demand": row["market_demand"], "avg_salary": round(row["regional_salary"], 2)} for row in regional_market.collect()]
        return JsonResponse({"status": "success", "monthly_trends": trends_result, "industry_demand": industry_result, "position_growth": growth_result, "company_activity": company_result, "regional_market": regional_result})

@csrf_exempt
def analyze_hot_skills(request):
    if request.method == 'POST':
        data = json.loads(request.body)
        skill_category = data.get('skill_category', 'all')
        analysis_period = data.get('analysis_period', '90')
        df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/job_data/skills_data.csv")
        df = df.filter(col("data_date") >= f"2024-{12-int(analysis_period)//30}-01")
        skills_exploded = df.select("position_id", "position_name", "salary_avg", "company_name", explode(split(col("required_skills"), ",")).alias("skill"))
        skills_exploded = skills_exploded.withColumn("skill", regexp_extract(col("skill"), r"\s*([^,]+)\s*", 1))
        if skill_category != 'all':
            category_keywords = {"programming": ["Python", "Java", "JavaScript", "Go", "C++"], "framework": ["TensorFlow", "PyTorch", "Django", "Spring", "Vue"], "database": ["MySQL", "PostgreSQL", "MongoDB", "Redis", "Elasticsearch"]}
            if skill_category in category_keywords:
                skills_exploded = skills_exploded.filter(col("skill").isin(category_keywords[skill_category]))
        skill_frequency = skills_exploded.groupBy("skill").agg(count("*").alias("mention_count"), avg(col("salary_avg")).alias("avg_skill_salary")).filter(col("mention_count") >= 5).orderBy(desc("mention_count"))
        skill_salary_correlation = skills_exploded.groupBy("skill").agg(avg(col("salary_avg")).alias("skill_avg_salary"), count("*").alias("skill_frequency")).filter(col("skill_frequency") >= 10).orderBy(desc("skill_avg_salary"))
        skill_combination = df.select("position_id", "required_skills", "salary_avg").rdd.flatMap(lambda row: [(tuple(sorted(row["required_skills"].split(",")[:3])), row["salary_avg"])]).groupByKey().mapValues(lambda salaries: (len(list(salaries)), sum(salaries)/len(list(salaries)))).filter(lambda x: x[1][0] >= 5).sortBy(lambda x: x[1][1], ascending=False)
        emerging_skills = skills_exploded.filter(col("data_date") >= "2024-11-01").groupBy("skill").agg(count("*").alias("recent_mentions")).filter(col("recent_mentions") >= 3).orderBy(desc("recent_mentions"))
        frequency_result = [{"skill": row["skill"], "count": row["mention_count"], "avg_salary": round(row["avg_skill_salary"], 2)} for row in skill_frequency.collect()[:20]]
        salary_result = [{"skill": row["skill"], "avg_salary": round(row["skill_avg_salary"], 2), "frequency": row["skill_frequency"]} for row in skill_salary_correlation.collect()[:15]]
        combination_result = [{"skills": "+".join(combo[0]), "frequency": combo[1][0], "avg_salary": round(combo[1][1], 2)} for combo in skill_combination.take(10)]
        emerging_result = [{"skill": row["skill"], "recent_mentions": row["recent_mentions"]} for row in emerging_skills.collect()[:10]]
        return JsonResponse({"status": "success", "skill_frequency": frequency_result, "skill_salary": salary_result, "skill_combinations": combination_result, "emerging_skills": emerging_result})

基于大数据的大模型岗位数据分析与可视化系统文档展示

💖💖作者：计算机毕业设计杰瑞
💙💙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
深度学校实战项目
计算机毕业设计选题推荐