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
《基于大数据的胡润榜全球企业估值分析与可视化系统》是一个融合现代大数据技术与企业经济分析的综合性平台。该系统采用Hadoop分布式存储架构配合Spark大数据处理引擎，构建了完整的企业估值数据分析流水线。系统前端运用Vue框架结合ElementUI组件库打造用户交互界面，通过ECharts可视化组件实现数据的直观展现，后端基于Spring Boot框架提供稳定的服务支撑，使用MyBatis进行数据持久化操作。核心功能模块涵盖企业竞争力分析、地理分布分析、行业趋势分析、估值分布分析等维度，通过Spark SQL进行复杂数据查询与统计计算，利用Pandas和NumPy库进行数据清洗与数值分析处理。系统设计了个人中心、用户管理、系统管理等基础功能模块，并构建了可视化大看板为决策者提供全景式的数据洞察视角。整个系统架构充分体现了大数据技术在企业经济分析领域的实际应用价值，为胡润榜企业数据的深度挖掘与智能分析提供了技术保障。
三、基于大数据的胡润榜全球企业估值分析与可视化系统-视频解说
企业级标准：基于大数据的胡润榜企业估值分析系统完整技术栈实现
四、基于大数据的胡润榜全球企业估值分析与可视化系统-功能展示
五、基于大数据的胡润榜全球企业估值分析与可视化系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, count, desc, asc, when, isnan, isnull
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("HurunEnterpriseAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def enterprise_competitiveness_analysis(enterprise_data_df):
"""企业竞争力分析核心功能"""
cleaned_df = enterprise_data_df.filter(col("valuation").isNotNull() & col("revenue").isNotNull() & col("industry").isNotNull())
industry_stats = cleaned_df.groupBy("industry").agg(avg("valuation").alias("avg_valuation"), avg("revenue").alias("avg_revenue"), count("*").alias("enterprise_count"))
enterprise_with_industry_avg = cleaned_df.join(industry_stats, "industry", "left")
competitiveness_df = enterprise_with_industry_avg.withColumn("valuation_score", when(col("valuation") > col("avg_valuation"), (col("valuation") / col("avg_valuation")) * 100).otherwise(50))
competitiveness_df = competitiveness_df.withColumn("revenue_score", when(col("revenue") > col("avg_revenue"), (col("revenue") / col("avg_revenue")) * 100).otherwise(50))
competitiveness_df = competitiveness_df.withColumn("market_share", col("revenue") / sum("revenue").over())
competitiveness_df = competitiveness_df.withColumn("market_position_score", when(col("market_share") > 0.05, 100).when(col("market_share") > 0.01, 75).otherwise(50))
final_score_df = competitiveness_df.withColumn("competitiveness_index", (col("valuation_score") * 0.4 + col("revenue_score") * 0.3 + col("market_position_score") * 0.3))
ranking_df = final_score_df.withColumn("ranking", row_number().over(Window.orderBy(desc("competitiveness_index"))))
competitive_enterprises = ranking_df.select("enterprise_name", "industry", "valuation", "revenue", "competitiveness_index", "ranking").orderBy(desc("competitiveness_index"))
industry_competition_intensity = ranking_df.groupBy("industry").agg(avg("competitiveness_index").alias("avg_competitiveness"), count("*").alias("enterprise_count"), (max("competitiveness_index") - min("competitiveness_index")).alias("competition_gap"))
result_dict = {"competitive_ranking": competitive_enterprises.limit(50).toPandas().to_dict('records'), "industry_competition": industry_competition_intensity.orderBy(desc("avg_competitiveness")).toPandas().to_dict('records')}
return result_dict
def geographical_distribution_analysis(enterprise_data_df):
"""地理分布分析核心功能"""
geo_cleaned_df = enterprise_data_df.filter(col("country").isNotNull() & col("city").isNotNull() & col("valuation").isNotNull())
country_distribution = geo_cleaned_df.groupBy("country").agg(count("*").alias("enterprise_count"), sum("valuation").alias("total_valuation"), avg("valuation").alias("avg_valuation"))
country_ranking = country_distribution.withColumn("valuation_density", col("total_valuation") / col("enterprise_count")).orderBy(desc("total_valuation"))
city_distribution = geo_cleaned_df.groupBy("country", "city").agg(count("*").alias("enterprise_count"), sum("valuation").alias("total_valuation"), avg("valuation").alias("avg_valuation"))
top_cities = city_distribution.withColumn("economic_influence", col("total_valuation") * col("enterprise_count") / 1000).orderBy(desc("economic_influence"))
regional_clusters = geo_cleaned_df.groupBy("region", "industry").agg(count("*").alias("cluster_size"), avg("valuation").alias("avg_cluster_valuation"))
industry_geo_correlation = regional_clusters.filter(col("cluster_size") > 5).orderBy(desc("avg_cluster_valuation"))
continent_summary = geo_cleaned_df.groupBy("continent").agg(count("*").alias("total_enterprises"), sum("valuation").alias("continent_valuation"), countDistinct("industry").alias("industry_diversity"))
cross_border_analysis = geo_cleaned_df.filter(col("has_international_business") == True).groupBy("country").agg(count("*").alias("international_enterprises"), avg("valuation").alias("avg_international_valuation"))
geo_concentration_index = country_distribution.select(col("enterprise_count"), sum("enterprise_count").over().alias("total_count")).withColumn("concentration_ratio", col("enterprise_count") / col("total_count"))
result_dict = {"country_ranking": country_ranking.limit(20).toPandas().to_dict('records'), "top_cities": top_cities.limit(30).toPandas().to_dict('records'), "industry_clusters": industry_geo_correlation.limit(25).toPandas().to_dict('records'), "continent_overview": continent_summary.toPandas().to_dict('records')}
return result_dict
def valuation_distribution_analysis(enterprise_data_df):
"""估值分布分析核心功能"""
valuation_df = enterprise_data_df.filter(col("valuation").isNotNull() & (col("valuation") > 0))
valuation_ranges = valuation_df.withColumn("valuation_tier", when(col("valuation") >= 100000, "Super Giant").when(col("valuation") >= 50000, "Giant").when(col("valuation") >= 10000, "Large").when(col("valuation") >= 1000, "Medium").otherwise("Small"))
tier_distribution = valuation_ranges.groupBy("valuation_tier").agg(count("*").alias("enterprise_count"), avg("valuation").alias("avg_tier_valuation"), sum("valuation").alias("total_tier_valuation"))
tier_analysis = tier_distribution.withColumn("market_share_percentage", col("total_tier_valuation") / sum("total_tier_valuation").over() * 100)
industry_valuation_stats = valuation_df.groupBy("industry").agg(avg("valuation").alias("industry_avg_valuation"), stddev("valuation").alias("valuation_volatility"), max("valuation").alias("industry_max_valuation"), min("valuation").alias("industry_min_valuation"))
valuation_growth_analysis = valuation_df.filter(col("previous_year_valuation").isNotNull()).withColumn("growth_rate", (col("valuation") - col("previous_year_valuation")) / col("previous_year_valuation") * 100)
growth_segments = valuation_growth_analysis.withColumn("growth_category", when(col("growth_rate") > 50, "High Growth").when(col("growth_rate") > 10, "Moderate Growth").when(col("growth_rate") > -10, "Stable").otherwise("Declining"))
growth_distribution = growth_segments.groupBy("growth_category", "industry").agg(count("*").alias("category_count"), avg("growth_rate").alias("avg_growth_rate"))
percentile_analysis = valuation_df.select(col("valuation"), percent_rank().over(Window.orderBy("valuation")).alias("percentile_rank"))
outlier_detection = valuation_df.select("*", ((col("valuation") - avg("valuation").over()) / stddev("valuation").over()).alias("z_score"))
high_value_outliers = outlier_detection.filter(col("z_score") > 2.5).select("enterprise_name", "industry", "valuation", "z_score")
result_dict = {"tier_distribution": tier_analysis.orderBy(desc("avg_tier_valuation")).toPandas().to_dict('records'), "industry_valuation": industry_valuation_stats.orderBy(desc("industry_avg_valuation")).toPandas().to_dict('records'), "growth_analysis": growth_distribution.orderBy(desc("avg_growth_rate")).toPandas().to_dict('records'), "market_outliers": high_value_outliers.orderBy(desc("valuation")).limit(15).toPandas().to_dict('records')}
return result_dict
六、基于大数据的胡润榜全球企业估值分析与可视化系统-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
