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
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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

AI就业影响数据可视化分析系统是一个基于大数据技术构建的智能分析平台，专门针对人工智能技术对就业市场产生的深层次影响进行全方位数据挖掘与可视化展示。系统采用Hadoop+Spark分布式计算框架作为数据处理核心，结合Python和Java双语言开发模式，通过Django和Spring Boot双后端架构提供稳定的服务支撑。前端运用Vue框架配合ElementUI组件库和Echarts图表库，构建出交互友好的数据展示界面。系统集成了个人信息管理、地理空间分析、行业洞察分析、AI影响评估分析、职位结构分析以及数据大屏展示六大核心功能模块，能够对海量就业数据进行实时处理和深度分析。通过Spark SQL和Pandas、NumPy等数据科学工具的协同运用，系统实现了对不同地区、不同行业、不同职位层级的AI技术渗透程度和就业影响程度的精准量化分析，为政策制定者、企业管理者和求职者提供科学的决策依据和趋势预测。

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 col, count, avg, sum, when, desc, asc
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType
import pandas as pd
import numpy as np
from datetime import datetime
import json

spark = SparkSession.builder.appName("AIJobImpactAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

def geographic_spatial_analysis(region_data, job_data, ai_adoption_data):
    region_df = spark.createDataFrame(region_data)
    job_df = spark.createDataFrame(job_data)
    ai_df = spark.createDataFrame(ai_adoption_data)
    merged_df = region_df.join(job_df, "region_code").join(ai_df, "region_code")
    ai_impact_by_region = merged_df.groupBy("region_name", "province").agg(
        count("job_id").alias("total_jobs"),
        avg("ai_adoption_rate").alias("avg_ai_adoption"),
        sum(when(col("job_status") == "replaced_by_ai", 1).otherwise(0)).alias("replaced_jobs"),
        sum(when(col("job_status") == "ai_augmented", 1).otherwise(0)).alias("augmented_jobs"),
        sum(when(col("job_status") == "ai_created", 1).otherwise(0)).alias("created_jobs")
    )
    ai_impact_by_region = ai_impact_by_region.withColumn(
        "replacement_rate", 
        col("replaced_jobs") / col("total_jobs") * 100
    ).withColumn(
        "augmentation_rate",
        col("augmented_jobs") / col("total_jobs") * 100
    ).withColumn(
        "creation_rate",
        col("created_jobs") / col("total_jobs") * 100
    )
    regional_clusters = ai_impact_by_region.withColumn(
        "impact_level",
        when(col("avg_ai_adoption") >= 0.7, "high_impact")
        .when(col("avg_ai_adoption") >= 0.4, "medium_impact")
        .otherwise("low_impact")
    )
    correlation_data = regional_clusters.select(
        "avg_ai_adoption", "replacement_rate", "augmentation_rate", "creation_rate"
    ).toPandas()
    correlation_matrix = correlation_data.corr()
    trend_analysis = merged_df.groupBy("region_name", "year").agg(
        avg("ai_adoption_rate").alias("yearly_adoption"),
        count("job_id").alias("yearly_jobs")
    ).orderBy("region_name", "year")
    spatial_hotspots = ai_impact_by_region.filter(
        (col("avg_ai_adoption") > 0.6) & (col("replacement_rate") > 10)
    ).orderBy(desc("avg_ai_adoption"))
    return {
        "regional_impact": regional_clusters.collect(),
        "correlation_matrix": correlation_matrix.to_dict(),
        "trend_data": trend_analysis.collect(),
        "hotspots": spatial_hotspots.collect()
    }

def industry_insight_analysis(industry_data, job_market_data, ai_technology_data):
    industry_df = spark.createDataFrame(industry_data)
    market_df = spark.createDataFrame(job_market_data)
    tech_df = spark.createDataFrame(ai_technology_data)
    combined_df = industry_df.join(market_df, "industry_code").join(tech_df, "industry_code")
    industry_ai_impact = combined_df.groupBy("industry_name", "industry_category").agg(
        count("position_id").alias("total_positions"),
        avg("ai_automation_score").alias("automation_risk"),
        avg("skill_complexity_score").alias("skill_complexity"),
        sum(when(col("position_trend") == "declining", col("position_count")).otherwise(0)).alias("declining_positions"),
        sum(when(col("position_trend") == "growing", col("position_count")).otherwise(0)).alias("growing_positions"),
        sum(when(col("ai_skill_required") == True, 1).otherwise(0)).alias("ai_skill_positions")
    )
    risk_assessment = industry_ai_impact.withColumn(
        "automation_risk_level",
        when(col("automation_risk") >= 0.8, "high_risk")
        .when(col("automation_risk") >= 0.5, "medium_risk")
        .otherwise("low_risk")
    ).withColumn(
        "transformation_index",
        (col("ai_skill_positions") / col("total_positions")) * col("automation_risk")
    )
    industry_evolution = combined_df.groupBy("industry_name", "quarter", "year").agg(
        avg("job_satisfaction_score").alias("avg_satisfaction"),
        avg("salary_level").alias("avg_salary"),
        count("new_hire_count").alias("hiring_activity"),
        sum("investment_in_ai").alias("ai_investment")
    ).orderBy("industry_name", "year", "quarter")
    competitive_analysis = industry_ai_impact.withColumn(
        "competitiveness_score",
        (col("growing_positions") - col("declining_positions")) / col("total_positions") * 100
    ).orderBy(desc("competitiveness_score"))
    skill_gap_analysis = combined_df.groupBy("industry_name").agg(
        count(when(col("skill_gap_exists") == True, 1)).alias("positions_with_gaps"),
        avg("training_investment").alias("avg_training_investment"),
        count(when(col("remote_work_possible") == True, 1)).alias("remote_friendly_positions")
    )
    future_projection = risk_assessment.withColumn(
        "projected_job_change",
        when(col("automation_risk") > 0.7, col("total_positions") * -0.3)
        .when(col("automation_risk") > 0.4, col("total_positions") * -0.1)
        .otherwise(col("total_positions") * 0.1)
    )
    return {
        "industry_impact": risk_assessment.collect(),
        "evolution_trends": industry_evolution.collect(),
        "competitive_landscape": competitive_analysis.collect(),
        "skill_gaps": skill_gap_analysis.collect(),
        "future_projections": future_projection.collect()
    }

def ai_impact_assessment_analysis(workforce_data, ai_deployment_data, economic_indicators):
    workforce_df = spark.createDataFrame(workforce_data)
    ai_deploy_df = spark.createDataFrame(ai_deployment_data)
    economic_df = spark.createDataFrame(economic_indicators)
    assessment_df = workforce_df.join(ai_deploy_df, "company_id").join(economic_df, "region_code")
    displacement_analysis = assessment_df.groupBy("occupation_category", "skill_level").agg(
        count("employee_id").alias("total_workforce"),
        sum(when(col("displacement_risk_score") >= 0.8, 1).otherwise(0)).alias("high_risk_workers"),
        sum(when(col("reskilling_completed") == True, 1).otherwise(0)).alias("reskilled_workers"),
        avg("productivity_change_percent").alias("avg_productivity_change"),
        avg("job_satisfaction_change").alias("avg_satisfaction_change")
    )
    temporal_impact_trends = assessment_df.groupBy("year", "quarter").agg(
        sum("jobs_eliminated").alias("quarterly_job_losses"),
        sum("jobs_created").alias("quarterly_job_creation"),
        avg("wage_change_percent").alias("avg_wage_change"),
        count(when(col("ai_tool_adoption") == True, 1)).alias("ai_adoption_count")
    ).withColumn(
        "net_job_impact",
        col("quarterly_job_creation") - col("quarterly_job_losses")
    ).orderBy("year", "quarter")
    demographic_impact = assessment_df.groupBy("age_group", "education_level", "gender").agg(
        count("employee_id").alias("group_size"),
        avg("displacement_probability").alias("avg_displacement_risk"),
        sum(when(col("career_transition_success") == True, 1).otherwise(0)).alias("successful_transitions"),
        avg("income_change_percent").alias("avg_income_change")
    )
    sectoral_vulnerability = assessment_df.groupBy("business_sector").agg(
        count("company_id").alias("companies_in_sector"),
        avg("ai_investment_ratio").alias("avg_ai_investment"),
        sum("workforce_reduction_count").alias("total_workforce_reduction"),
        sum("new_role_creation_count").alias("total_new_roles"),
        avg("operational_efficiency_gain").alias("avg_efficiency_gain")
    ).withColumn(
        "vulnerability_index",
        col("total_workforce_reduction") / (col("total_workforce_reduction") + col("total_new_roles"))
    )
    mitigation_effectiveness = assessment_df.groupBy("mitigation_strategy_type").agg(
        count("employee_id").alias("strategy_coverage"),
        avg("post_strategy_employability").alias("avg_employability_improvement"),
        sum(when(col("strategy_success") == True, 1).otherwise(0)).alias("successful_cases"),
        avg("strategy_cost_per_employee").alias("avg_strategy_cost")
    ).withColumn(
        "strategy_effectiveness",
        col("successful_cases") / col("strategy_coverage") * 100
    )
    comprehensive_impact_score = assessment_df.select(
        "region_code",
        ((col("displacement_risk_score") * 0.4) + 
         (col("economic_disruption_score") * 0.3) + 
         (col("social_adaptation_score") * 0.3)).alias("comprehensive_impact_score")
    ).groupBy("region_code").avg("comprehensive_impact_score")
    return {
        "displacement_analysis": displacement_analysis.collect(),
        "temporal_trends": temporal_impact_trends.collect(),
        "demographic_impact": demographic_impact.collect(),
        "sectoral_vulnerability": sectoral_vulnerability.collect(),
        "mitigation_effectiveness": mitigation_effectiveness.collect(),
        "comprehensive_scores": comprehensive_impact_score.collect()
    }

六、部分文档展示

七、END

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