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
基于大数据的全球经济指标数据分析与可视化系统是一个运用现代大数据技术处理全球经济信息的综合性平台。该系统采用Hadoop分布式存储架构和Spark内存计算引擎，能够高效处理海量的全球经济指标数据，通过Python数据科学库进行深度分析，结合Django后端框架构建稳定的服务架构。前端采用Vue.js配合ElementUI组件库和Echarts图表库，为用户提供直观的数据可视化界面。系统核心功能涵盖经济指标数据的采集、清洗、存储、分析和展示全流程，支持国家经济画像分析、全球经济格局分析、政府财政健康分析和宏观经济健康分析等多维度经济数据挖掘。通过HDFS分布式文件系统存储大规模经济数据，利用Spark SQL进行高性能数据查询和分析，结合Pandas和NumPy进行科学计算，最终通过MySQL数据库管理结构化数据，为经济研究和决策提供数据支撑和可视化展示。
三、基于大数据的全球经济指标数据分析与可视化系统-视频解说
数据可视化爆火后：全球经济指标大数据系统成了计算机毕设首选|系统|计算机毕设|毕业设计
四、基于大数据的全球经济指标数据分析与可视化系统-功能展示
五、基于大数据的全球经济指标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, sum, count, desc, asc
import pandas as pd
import numpy as np
from django.http import JsonResponse
import json
spark = SparkSession.builder.appName("GlobalEconomicAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def economic_indicator_data_management(request):
if request.method == 'POST':
data = json.loads(request.body)
country_name = data.get('country_name')
gdp_value = data.get('gdp_value')
inflation_rate = data.get('inflation_rate')
unemployment_rate = data.get('unemployment_rate')
trade_balance = data.get('trade_balance')
fiscal_deficit = data.get('fiscal_deficit')
debt_ratio = data.get('debt_ratio')
year = data.get('year')
quarter = data.get('quarter')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/economic_db").option("dbtable", "economic_indicators").option("user", "root").option("password", "password").load()
new_data = spark.createDataFrame([(country_name, gdp_value, inflation_rate, unemployment_rate, trade_balance, fiscal_deficit, debt_ratio, year, quarter)], ["country", "gdp", "inflation", "unemployment", "trade_balance", "fiscal_deficit", "debt_ratio", "year", "quarter"])
updated_df = df.union(new_data)
validation_result = updated_df.filter((col("gdp") < 0) | (col("inflation") > 50) | (col("unemployment") > 30)).count()
if validation_result > 0:
return JsonResponse({'status': 'error', 'message': '数据验证失败，存在异常值'})
processed_df = updated_df.withColumn("gdp_growth_rate", (col("gdp") - col("gdp").lag(1).over(Window.partitionBy("country").orderBy("year", "quarter"))) / col("gdp").lag(1).over(Window.partitionBy("country").orderBy("year", "quarter")) * 100)
processed_df = processed_df.withColumn("economic_stability_index", (col("inflation") * 0.3 + col("unemployment") * 0.4 + abs(col("trade_balance")) / col("gdp") * 100 * 0.3))
processed_df.write.format("jdbc").option("url", "jdbc:mysql://localhost:3306/economic_db").option("dbtable", "economic_indicators").option("user", "root").option("password", "password").mode("overwrite").save()
return JsonResponse({'status': 'success', 'message': '经济指标数据处理完成', 'processed_records': processed_df.count()})
def country_economic_analysis(request):
country_code = request.GET.get('country_code')
analysis_period = request.GET.get('period', '5')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/economic_db").option("dbtable", "economic_indicators").option("user", "root").option("password", "password").load()
country_df = df.filter(col("country") == country_code).orderBy(desc("year"), desc("quarter")).limit(int(analysis_period) * 4)
gdp_trend = country_df.select("year", "quarter", "gdp").orderBy("year", "quarter").collect()
gdp_growth_analysis = country_df.select(avg("gdp_growth_rate").alias("avg_growth"), stddev("gdp_growth_rate").alias("growth_volatility")).collect()[0]
inflation_analysis = country_df.select(avg("inflation").alias("avg_inflation"), max("inflation").alias("max_inflation"), min("inflation").alias("min_inflation")).collect()[0]
unemployment_trend = country_df.select("year", "unemployment").groupBy("year").agg(avg("unemployment").alias("annual_unemployment")).orderBy("year").collect()
fiscal_health = country_df.select(avg("fiscal_deficit").alias("avg_deficit"), avg("debt_ratio").alias("avg_debt_ratio")).collect()[0]
trade_performance = country_df.select(sum("trade_balance").alias("total_trade_balance"), avg("trade_balance").alias("avg_trade_balance")).collect()[0]
economic_risk_score = country_df.select(avg(col("inflation") * 0.25 + col("unemployment") * 0.35 + abs(col("fiscal_deficit")) * 0.2 + col("debt_ratio") * 0.2).alias("risk_score")).collect()[0].risk_score
competitiveness_rank = df.groupBy("country").agg(avg("gdp_growth_rate").alias("avg_growth"), avg("economic_stability_index").alias("stability")).orderBy(desc("avg_growth"), asc("stability")).collect()
country_rank = next((i + 1 for i, row in enumerate(competitiveness_rank) if row.country == country_code), None)
policy_recommendations = []
if inflation_analysis.avg_inflation > 5:
policy_recommendations.append("建议实施紧缩性货币政策控制通胀")
if unemployment_trend[-1].annual_unemployment > 8:
policy_recommendations.append("建议加大就业创造和技能培训投入")
if fiscal_health.avg_debt_ratio > 60:
policy_recommendations.append("建议优化财政支出结构降低债务比率")
return JsonResponse({'gdp_trend': [row.asDict() for row in gdp_trend], 'growth_analysis': gdp_growth_analysis.asDict(), 'inflation_analysis': inflation_analysis.asDict(), 'unemployment_trend': [row.asDict() for row in unemployment_trend], 'fiscal_health': fiscal_health.asDict(), 'trade_performance': trade_performance.asDict(), 'risk_score': economic_risk_score, 'global_rank': country_rank, 'recommendations': policy_recommendations})
def global_economic_pattern_analysis(request):
analysis_year = request.GET.get('year', '2023')
region_focus = request.GET.get('region', 'all')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/economic_db").option("dbtable", "economic_indicators").option("user", "root").option("password", "password").load()
yearly_df = df.filter(col("year") == analysis_year)
if region_focus != 'all':
yearly_df = yearly_df.filter(col("region") == region_focus)
global_gdp_distribution = yearly_df.select("country", "gdp").orderBy(desc("gdp")).limit(20).collect()
total_global_gdp = yearly_df.agg(sum("gdp").alias("total")).collect()[0].total
gdp_concentration = yearly_df.orderBy(desc("gdp")).limit(10).agg(sum("gdp").alias("top10_gdp")).collect()[0].top10_gdp / total_global_gdp * 100
regional_performance = yearly_df.groupBy("region").agg(avg("gdp_growth_rate").alias("avg_growth"), avg("inflation").alias("avg_inflation"), avg("unemployment").alias("avg_unemployment"), sum("gdp").alias("regional_gdp")).orderBy(desc("regional_gdp")).collect()
economic_correlation_matrix = yearly_df.select("gdp_growth_rate", "inflation", "unemployment", "trade_balance").toPandas().corr()
trade_flow_analysis = yearly_df.groupBy("country").agg(sum("trade_balance").alias("net_trade")).orderBy(desc("net_trade")).collect()
surplus_countries = [row for row in trade_flow_analysis if row.net_trade > 0]
deficit_countries = [row for row in trade_flow_analysis if row.net_trade < 0]
inflation_clusters = yearly_df.select("country", "inflation").collect()
low_inflation = [row.country for row in inflation_clusters if row.inflation < 2]
moderate_inflation = [row.country for row in inflation_clusters if 2 <= row.inflation <= 5]
high_inflation = [row.country for row in inflation_clusters if row.inflation > 5]
economic_development_levels = yearly_df.withColumn("development_level", when(col("gdp") > 1000000, "发达经济体").when(col("gdp") > 100000, "新兴经济体").otherwise("发展中经济体")).groupBy("development_level").agg(count("country").alias("country_count"), avg("gdp_growth_rate").alias("avg_growth")).collect()
vulnerability_index = yearly_df.withColumn("vulnerability", col("debt_ratio") * 0.4 + abs(col("fiscal_deficit")) * 0.3 + col("inflation") * 0.3).select("country", "vulnerability").orderBy(desc("vulnerability")).limit(15).collect()
return JsonResponse({'global_gdp_ranking': [{'country': row.country, 'gdp': row.gdp} for row in global_gdp_distribution], 'gdp_concentration_ratio': gdp_concentration, 'regional_analysis': [row.asDict() for row in regional_performance], 'correlation_matrix': economic_correlation_matrix.to_dict(), 'trade_surplus_countries': [{'country': row.country, 'surplus': row.net_trade} for row in surplus_countries[:10]], 'trade_deficit_countries': [{'country': row.country, 'deficit': row.net_trade} for row in deficit_countries[:10]], 'inflation_distribution': {'low': low_inflation, 'moderate': moderate_inflation, 'high': high_inflation}, 'development_levels': [row.asDict() for row in economic_development_levels], 'vulnerable_economies': [{'country': row.country, 'vulnerability_score': row.vulnerability} for row in vulnerability_index]})
六、基于大数据的全球经济指标数据分析与可视化系统-文档展示
七、END
💛💛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
