💖💖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AI就业影响数据可视化分析系统介绍
基于大数据的AI就业影响数据可视化分析系统是一个综合运用Hadoop分布式存储、Spark大数据计算引擎以及Python数据分析技术的毕业设计项目。该系统采用Django作为后端框架，结合MySQL数据库进行数据管理，前端通过Vue框架配合ElementUI组件库和Echarts图表库实现用户交互界面。系统核心功能围绕AI技术对就业市场影响的数据收集、存储、分析和可视化展示展开，通过HDFS分布式文件系统存储海量就业相关数据，利用Spark SQL进行高效的数据查询和分析处理，结合Pandas和NumPy等Python科学计算库进行数据清洗和统计分析。用户可以通过系统首页快速了解整体数据概况，在数据可视化模块中查看各类就业影响指标的图表展示，通过我的信息模块管理个人设置，系统管理模块则提供基础的用户权限和数据管理功能。整个系统架构体现了现代大数据技术栈的综合应用，为分析AI技术发展对就业市场的深层影响提供了技术支撑和数据洞察平台。
基于大数据的AI就业影响数据可视化分析系统演示视频
基于大数据的AI就业影响数据可视化分析系统演示图片
基于大数据的AI就业影响数据可视化分析系统代码展示
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 import View
import json
spark = SparkSession.builder.appName("AIJobImpactAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def process_job_data_collection(request):
raw_data_path = "hdfs://localhost:9000/ai_job_data/raw/"
processed_data_path = "hdfs://localhost:9000/ai_job_data/processed/"
job_schema = StructType([
StructField("job_id", StringType(), True),
StructField("job_title", StringType(), True),
StructField("industry", StringType(), True),
StructField("ai_related", BooleanType(), True),
StructField("salary_range", StringType(), True),
StructField("experience_required", StringType(), True),
StructField("location", StringType(), True),
StructField("post_date", DateType(), True),
StructField("company_size", StringType(), True),
StructField("skills_required", ArrayType(StringType()), True)
])
raw_df = spark.read.option("header", "true").schema(job_schema).csv(raw_data_path)
cleaned_df = raw_df.filter(col("job_title").isNotNull() & col("industry").isNotNull())
cleaned_df = cleaned_df.withColumn("salary_numeric", regexp_extract(col("salary_range"), r"(\d+)", 1).cast(IntegerType()))
cleaned_df = cleaned_df.withColumn("ai_impact_score", when(col("ai_related") == True, 0.8).when(array_contains(col("skills_required"), "machine learning"), 0.6).when(array_contains(col("skills_required"), "python"), 0.4).otherwise(0.2))
cleaned_df = cleaned_df.withColumn("year_month", date_format(col("post_date"), "yyyy-MM"))
processed_df = cleaned_df.groupBy("industry", "year_month", "ai_related").agg(
count("job_id").alias("job_count"),
avg("salary_numeric").alias("avg_salary"),
avg("ai_impact_score").alias("avg_ai_impact")
)
processed_df.write.mode("overwrite").option("header", "true").csv(processed_data_path)
job_trend_data = processed_df.collect()
result_data = []
for row in job_trend_data:
result_data.append({
"industry": row["industry"],
"year_month": row["year_month"],
"ai_related": row["ai_related"],
"job_count": row["job_count"],
"avg_salary": round(row["avg_salary"], 2) if row["avg_salary"] else 0,
"avg_ai_impact": round(row["avg_ai_impact"], 3)
})
return JsonResponse({"status": "success", "data": result_data, "total_records": len(result_data)})
def analyze_employment_impact(request):
processed_data_path = "hdfs://localhost:9000/ai_job_data/processed/"
impact_analysis_path = "hdfs://localhost:9000/ai_job_data/analysis/"
employment_df = spark.read.option("header", "true").csv(processed_data_path)
employment_df = employment_df.withColumn("job_count", col("job_count").cast(IntegerType()))
employment_df = employment_df.withColumn("avg_salary", col("avg_salary").cast(DoubleType()))
employment_df = employment_df.withColumn("avg_ai_impact", col("avg_ai_impact").cast(DoubleType()))
industry_impact = employment_df.groupBy("industry").agg(
sum("job_count").alias("total_jobs"),
avg("avg_salary").alias("industry_avg_salary"),
avg("avg_ai_impact").alias("industry_ai_impact"),
countDistinct("year_month").alias("data_months")
)
ai_vs_traditional = employment_df.groupBy("ai_related").agg(
sum("job_count").alias("category_jobs"),
avg("avg_salary").alias("category_salary"),
avg("avg_ai_impact").alias("category_impact")
)
monthly_trend = employment_df.groupBy("year_month").agg(
sum("job_count").alias("monthly_jobs"),
avg("avg_salary").alias("monthly_salary"),
avg("avg_ai_impact").alias("monthly_impact")
).orderBy("year_month")
correlation_analysis = employment_df.stat.corr("job_count", "avg_ai_impact")
salary_impact_correlation = employment_df.stat.corr("avg_salary", "avg_ai_impact")
impact_summary = industry_impact.withColumn("impact_level",
when(col("industry_ai_impact") >= 0.6, "High")
.when(col("industry_ai_impact") >= 0.4, "Medium")
.otherwise("Low")
)
impact_summary.write.mode("overwrite").option("header", "true").csv(impact_analysis_path + "/industry_impact")
monthly_trend.write.mode("overwrite").option("header", "true").csv(impact_analysis_path + "/monthly_trend")
analysis_results = {
"industry_analysis": [row.asDict() for row in industry_impact.collect()],
"ai_comparison": [row.asDict() for row in ai_vs_traditional.collect()],
"monthly_trends": [row.asDict() for row in monthly_trend.collect()],
"correlations": {
"jobs_ai_correlation": round(correlation_analysis, 4),
"salary_ai_correlation": round(salary_impact_correlation, 4)
}
}
return JsonResponse({"status": "success", "analysis_data": analysis_results})
def generate_visualization_data(request):
analysis_data_path = "hdfs://localhost:9000/ai_job_data/analysis/"
visualization_path = "hdfs://localhost:9000/ai_job_data/visualization/"
industry_df = spark.read.option("header", "true").csv(analysis_data_path + "/industry_impact")
monthly_df = spark.read.option("header", "true").csv(analysis_data_path + "/monthly_trend")
industry_chart_data = industry_df.select("industry", "total_jobs", "industry_avg_salary", "industry_ai_impact").collect()
chart_industries = [row["industry"] for row in industry_chart_data]
chart_job_counts = [int(row["total_jobs"]) for row in industry_chart_data]
chart_salaries = [round(float(row["industry_avg_salary"]), 2) for row in industry_chart_data]
chart_ai_impacts = [round(float(row["industry_ai_impact"]), 3) for row in industry_chart_data]
monthly_chart_data = monthly_df.select("year_month", "monthly_jobs", "monthly_salary", "monthly_impact").orderBy("year_month").collect()
chart_months = [row["year_month"] for row in monthly_chart_data]
chart_monthly_jobs = [int(row["monthly_jobs"]) for row in monthly_chart_data]
chart_monthly_salaries = [round(float(row["monthly_salary"]), 2) for row in monthly_chart_data]
chart_monthly_impacts = [round(float(row["monthly_impact"]), 3) for row in monthly_chart_data]
top_industries = industry_df.orderBy(desc("total_jobs")).limit(10)
top_impact_industries = industry_df.orderBy(desc("industry_ai_impact")).limit(8)
pie_chart_data = []
for row in top_industries.collect():
pie_chart_data.append({
"name": row["industry"],
"value": int(row["total_jobs"]),
"ai_impact": round(float(row["industry_ai_impact"]), 3)
})
heatmap_data = []
for i, industry in enumerate(chart_industries):
for j, month in enumerate(chart_months):
impact_value = chart_ai_impacts[i] * (chart_monthly_impacts[j] if j < len(chart_monthly_impacts) else 0.5)
heatmap_data.append([j, i, round(impact_value, 3)])
visualization_result = {
"bar_chart": {
"categories": chart_industries,
"job_counts": chart_job_counts,
"salaries": chart_salaries,
"ai_impacts": chart_ai_impacts
},
"line_chart": {
"months": chart_months,
"job_trends": chart_monthly_jobs,
"salary_trends": chart_monthly_salaries,
"impact_trends": chart_monthly_impacts
},
"pie_chart": pie_chart_data,
"heatmap": {
"data": heatmap_data,
"x_axis": chart_months,
"y_axis": chart_industries
}
}
return JsonResponse({"status": "success", "visualization_data": visualization_result})
基于大数据的AI就业影响数据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
