💖💖作者：计算机编程小咖 💙💙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
@TOC
胡润榜全球企业估值分析与可视化系统介绍
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集数据处理、分析与可视化于一体的综合性大数据应用平台，该系统采用Hadoop+Spark大数据框架作为核心技术架构，充分发挥分布式计算的优势来处理海量的胡润榜企业数据。系统支持Python+Django和Java+Spring Boot两种开发方案，前端采用Vue+ElementUI+Echarts技术栈构建用户界面，实现了数据的动态可视化展示。在数据处理层面，系统运用Hadoop分布式文件系统HDFS进行数据存储，结合Spark和Spark SQL进行高效的数据计算与查询，同时集成Pandas和NumPy等Python科学计算库进行数据分析处理，底层数据库采用MySQL进行结构化数据管理。功能方面，系统提供了完善的用户管理模块，包括个人中心、密码修改、个人信息管理等基础功能，核心分析功能涵盖企业竞争力分析、地理分布分析、行业趋势分析和估值分布分析四大维度，能够从多角度深入挖掘胡润榜企业数据的价值规律。系统还配备了系统管理模块确保平台稳定运行，最具特色的是大屏可视化功能，通过Echarts图表库将复杂的企业估值数据转化为直观的图表展示，为用户提供清晰的数据洞察体验，整个系统充分体现了大数据技术在商业数据分析领域的实际应用价值。
胡润榜全球企业估值分析与可视化系统演示视频
胡润榜全球企业估值分析与可视化系统演示图片
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, when, year, month
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, IntegerType
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("HurunEnterpriseAnalysis").master("local[*]").getOrCreate()
@csrf_exempt
def enterprise_competitiveness_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
industry = data.get('industry', '')
region = data.get('region', '')
year_range = data.get('year_range', [2020, 2024])
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hurun_db").option("dbtable", "enterprise_info").option("user", "root").option("password", "123456").load()
filtered_df = df.filter((col("industry") == industry) & (col("region") == region) & (col("year").between(year_range[0], year_range[1])))
competitiveness_df = filtered_df.select("enterprise_name", "valuation", "revenue", "profit", "employee_count", "year").withColumn("revenue_per_employee", col("revenue") / col("employee_count")).withColumn("profit_margin", col("profit") / col("revenue") * 100).withColumn("valuation_growth", col("valuation") - col("valuation").lag(1).over(Window.partitionBy("enterprise_name").orderBy("year")))
competitiveness_score = competitiveness_df.withColumn("competitiveness_score", (col("profit_margin") * 0.3 + col("revenue_per_employee") / 10000 * 0.3 + col("valuation_growth") / col("valuation") * 100 * 0.4)).select("enterprise_name", "competitiveness_score", "valuation", "profit_margin", "revenue_per_employee").orderBy(desc("competitiveness_score"))
top_enterprises = competitiveness_score.limit(20).collect()
avg_score = competitiveness_df.agg(avg("competitiveness_score")).collect()[0][0]
industry_benchmark = competitiveness_df.groupBy("industry").agg(avg("competitiveness_score").alias("industry_avg_score")).collect()
result_data = []
for row in top_enterprises:
result_data.append({"enterprise_name": row["enterprise_name"], "competitiveness_score": round(row["competitiveness_score"], 2), "valuation": row["valuation"], "profit_margin": round(row["profit_margin"], 2), "revenue_per_employee": round(row["revenue_per_employee"], 2)})
spark.stop()
return JsonResponse({"status": "success", "data": result_data, "avg_score": round(avg_score, 2), "industry_benchmark": [{"industry": row["industry"], "avg_score": round(row["industry_avg_score"], 2)} for row in industry_benchmark]})
@csrf_exempt
def geographical_distribution_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
analysis_type = data.get('analysis_type', 'country')
year = data.get('year', 2024)
min_valuation = data.get('min_valuation', 0)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hurun_db").option("dbtable", "enterprise_info").option("user", "root").option("password", "123456").load()
filtered_df = df.filter((col("year") == year) & (col("valuation") >= min_valuation))
if analysis_type == 'country':
geo_stats = filtered_df.groupBy("country").agg(count("enterprise_name").alias("enterprise_count"), sum("valuation").alias("total_valuation"), avg("valuation").alias("avg_valuation")).orderBy(desc("total_valuation"))
else:
geo_stats = filtered_df.groupBy("region").agg(count("enterprise_name").alias("enterprise_count"), sum("valuation").alias("total_valuation"), avg("valuation").alias("avg_valuation")).orderBy(desc("total_valuation"))
industry_distribution = filtered_df.groupBy("country" if analysis_type == 'country' else "region", "industry").agg(count("enterprise_name").alias("count")).orderBy("country" if analysis_type == 'country' else "region", desc("count"))
valuation_ranges = filtered_df.withColumn("valuation_range", when(col("valuation") < 1000000000, "10亿以下").when((col("valuation") >= 1000000000) & (col("valuation") < 5000000000), "10-50亿").when((col("valuation") >= 5000000000) & (col("valuation") < 10000000000), "50-100亿").otherwise("100亿以上")).groupBy("country" if analysis_type == 'country' else "region", "valuation_range").agg(count("enterprise_name").alias("count"))
top_regions = geo_stats.limit(15).collect()
industry_dist_data = industry_distribution.collect()
valuation_range_data = valuation_ranges.collect()
total_enterprises = filtered_df.count()
total_valuation = filtered_df.agg(sum("valuation")).collect()[0][0]
concentration_index = sum([(row["enterprise_count"] / total_enterprises) ** 2 for row in top_regions])
result_data = []
for row in top_regions:
result_data.append({"region": row[analysis_type], "enterprise_count": row["enterprise_count"], "total_valuation": round(row["total_valuation"] / 1000000000, 2), "avg_valuation": round(row["avg_valuation"] / 1000000000, 2), "market_share": round(row["total_valuation"] / total_valuation * 100, 2)})
industry_result = [{"region": row[0], "industry": row["industry"], "count": row["count"]} for row in industry_dist_data]
valuation_result = [{"region": row[0], "valuation_range": row["valuation_range"], "count": row["count"]} for row in valuation_range_data]
spark.stop()
return JsonResponse({"status": "success", "geo_data": result_data, "industry_distribution": industry_result, "valuation_distribution": valuation_result, "concentration_index": round(concentration_index, 4), "analysis_summary": {"total_enterprises": total_enterprises, "total_valuation": round(total_valuation / 1000000000, 2)}})
@csrf_exempt
def industry_trend_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
target_industries = data.get('industries', [])
start_year = data.get('start_year', 2020)
end_year = data.get('end_year', 2024)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hurun_db").option("dbtable", "enterprise_info").option("user", "root").option("password", "123456").load()
filtered_df = df.filter((col("year").between(start_year, end_year)) & (col("industry").isin(target_industries)))
yearly_trends = filtered_df.groupBy("industry", "year").agg(count("enterprise_name").alias("enterprise_count"), sum("valuation").alias("total_valuation"), avg("valuation").alias("avg_valuation"), sum("revenue").alias("total_revenue"), avg("profit_margin").alias("avg_profit_margin")).orderBy("industry", "year")
growth_rates = yearly_trends.withColumn("valuation_growth_rate", (col("total_valuation") - col("total_valuation").lag(1).over(Window.partitionBy("industry").orderBy("year"))) / col("total_valuation").lag(1).over(Window.partitionBy("industry").orderBy("year")) * 100).withColumn("enterprise_growth_rate", (col("enterprise_count") - col("enterprise_count").lag(1).over(Window.partitionBy("industry").orderBy("year"))) / col("enterprise_count").lag(1).over(Window.partitionBy("industry").orderBy("year")) * 100)
industry_rankings = filtered_df.filter(col("year") == end_year).groupBy("industry").agg(sum("valuation").alias("current_total_valuation"), count("enterprise_name").alias("current_enterprise_count"), avg("profit_margin").alias("current_avg_profit_margin")).orderBy(desc("current_total_valuation"))
market_concentration = filtered_df.groupBy("industry", "year").agg(sum("valuation").alias("industry_total")).join(filtered_df.groupBy("year").agg(sum("valuation").alias("market_total")), "year").withColumn("market_share", col("industry_total") / col("market_total") * 100)
emerging_trends = filtered_df.filter(col("year").isin([start_year, end_year])).groupBy("industry").agg(avg("valuation").alias("avg_valuation"), count("enterprise_name").alias("enterprise_count")).withColumn("growth_potential", col("avg_valuation") * col("enterprise_count") / 1000000000)
trend_data = yearly_trends.collect()
growth_data = growth_rates.filter(col("valuation_growth_rate").isNotNull()).collect()
ranking_data = industry_rankings.collect()
concentration_data = market_concentration.collect()
emerging_data = emerging_trends.orderBy(desc("growth_potential")).collect()
result_trends = []
for row in trend_data:
result_trends.append({"industry": row["industry"], "year": row["year"], "enterprise_count": row["enterprise_count"], "total_valuation": round(row["total_valuation"] / 1000000000, 2), "avg_valuation": round(row["avg_valuation"] / 1000000000, 2), "total_revenue": round(row["total_revenue"] / 1000000000, 2), "avg_profit_margin": round(row["avg_profit_margin"], 2)})
growth_trends = [{"industry": row["industry"], "year": row["year"], "valuation_growth_rate": round(row["valuation_growth_rate"], 2), "enterprise_growth_rate": round(row["enterprise_growth_rate"], 2)} for row in growth_data]
industry_rank = [{"industry": row["industry"], "total_valuation": round(row["current_total_valuation"] / 1000000000, 2), "enterprise_count": row["current_enterprise_count"], "avg_profit_margin": round(row["current_avg_profit_margin"], 2)} for row in ranking_data]
spark.stop()
return JsonResponse({"status": "success", "trend_analysis": result_trends, "growth_analysis": growth_trends, "industry_rankings": industry_rank, "market_concentration": [{"industry": row["industry"], "year": row["year"], "market_share": round(row["market_share"], 2)} for row in concentration_data], "emerging_potential": [{"industry": row["industry"], "growth_potential": round(row["growth_potential"], 2)} for row in emerging_data[:10]]})
胡润榜全球企业估值分析与可视化系统文档展示
💖💖作者：计算机编程小咖 💙💙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
