💖💖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
基于大数据的胡润榜全球企业估值分析系统介绍
《胡润榜全球企业估值分析系统》是一个基于大数据技术栈构建的企业价值分析平台，采用Hadoop+Spark分布式计算框架处理海量企业数据，通过Python和Django框架实现后端业务逻辑，前端采用Vue+ElementUI+Echarts技术栈提供直观的数据可视化界面。系统核心功能涵盖个人中心、用户管理、企业竞争力分析、地理分布分析、行业趋势分析、估值分布分析和系统管理等模块，能够对全球范围内的企业进行多维度估值分析和竞争力评估。系统利用HDFS存储大规模企业数据，通过Spark SQL进行高效的数据查询和分析，结合Pandas和NumPy进行数据处理和统计计算，最终将分析结果以图表形式展示给用户，为投资决策和市场研究提供数据支撑。
基于大数据的胡润榜全球企业估值分析系统演示视频
基于大数据的胡润榜全球企业估值分析系统演示图片
基于大数据的胡润榜全球企业估值分析系统代码展示
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
from django.http import JsonResponse
from django.views import View
import json
spark = SparkSession.builder.appName("HurunCompanyAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
class CompetitiveAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
company_id = data.get('company_id')
analysis_type = data.get('analysis_type')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hurun_db").option("dbtable", "company_info").option("user", "root").option("password", "123456").load()
company_df = df.filter(col("company_id") == company_id)
industry_df = df.filter(col("industry") == company_df.select("industry").first()[0])
revenue_rank = industry_df.select("company_id", "revenue").orderBy(desc("revenue")).withColumn("rank", row_number().over(Window.orderBy(desc("revenue"))))
market_share = company_df.select("revenue").first()[0] / industry_df.agg(sum("revenue")).first()[0] * 100
growth_rate_df = df.select("company_id", ((col("revenue_current") - col("revenue_previous")) / col("revenue_previous") * 100).alias("growth_rate"))
company_growth = growth_rate_df.filter(col("company_id") == company_id).select("growth_rate").first()[0]
industry_avg_growth = growth_rate_df.filter(col("company_id").isin([row.company_id for row in industry_df.collect()])).agg(avg("growth_rate")).first()[0]
profitability_score = (company_df.select("profit_margin").first()[0] * 0.4 + company_df.select("roe").first()[0] * 0.3 + company_df.select("roa").first()[0] * 0.3)
innovation_score = (company_df.select("rd_investment").first()[0] / company_df.select("revenue").first()[0]) * 100
financial_stability = (company_df.select("debt_ratio").first()[0] * 0.5 + company_df.select("current_ratio").first()[0] * 0.3 + company_df.select("quick_ratio").first()[0] * 0.2)
competitive_score = profitability_score * 0.4 + innovation_score * 0.3 + financial_stability * 0.3
result = {"market_share": round(market_share, 2), "growth_rate": round(company_growth, 2), "industry_avg_growth": round(industry_avg_growth, 2), "competitive_score": round(competitive_score, 2), "profitability_score": round(profitability_score, 2), "innovation_score": round(innovation_score, 2)}
return JsonResponse(result)
class GeographicDistributionView(View):
def post(self, request):
data = json.loads(request.body)
region_type = data.get('region_type', 'country')
industry_filter = data.get('industry_filter', None)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hurun_db").option("dbtable", "company_info").option("user", "root").option("password", "123456").load()
if industry_filter:
df = df.filter(col("industry") == industry_filter)
if region_type == 'country':
region_stats = df.groupBy("country").agg(count("company_id").alias("company_count"), sum("valuation").alias("total_valuation"), avg("valuation").alias("avg_valuation"), sum("employee_count").alias("total_employees"))
else:
region_stats = df.groupBy("city").agg(count("company_id").alias("company_count"), sum("valuation").alias("total_valuation"), avg("valuation").alias("avg_valuation"), sum("employee_count").alias("total_employees"))
top_regions = region_stats.orderBy(desc("total_valuation")).limit(20)
valuation_distribution = df.select("country", "valuation").groupBy("country").agg(collect_list("valuation").alias("valuations"))
concentration_index = region_stats.select((col("total_valuation") / region_stats.agg(sum("total_valuation")).first()[0]).alias("concentration")).agg(sum(col("concentration") * col("concentration"))).first()[0]
geographic_diversity = region_stats.count()
industry_by_region = df.groupBy("country", "industry").agg(count("company_id").alias("count")).orderBy("country", desc("count"))
growth_by_region = df.select("country", ((col("valuation_current") - col("valuation_previous")) / col("valuation_previous") * 100).alias("growth_rate")).groupBy("country").agg(avg("growth_rate").alias("avg_growth_rate")).orderBy(desc("avg_growth_rate"))
pandas_result = top_regions.toPandas()
result_data = pandas_result.to_dict('records')
for item in result_data:
item['total_valuation'] = float(item['total_valuation']) if item['total_valuation'] else 0
item['avg_valuation'] = float(item['avg_valuation']) if item['avg_valuation'] else 0
result = {"top_regions": result_data, "concentration_index": round(concentration_index, 4), "geographic_diversity": geographic_diversity}
return JsonResponse(result)
class ValuationDistributionView(View):
def post(self, request):
data = json.loads(request.body)
valuation_range = data.get('valuation_range', 'all')
industry_filter = data.get('industry_filter', None)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hurun_db").option("dbtable", "company_info").option("user", "root").option("password", "123456").load()
if industry_filter:
df = df.filter(col("industry") == industry_filter)
valuation_stats = df.agg(min("valuation").alias("min_val"), max("valuation").alias("max_val"), avg("valuation").alias("avg_val"), stddev("valuation").alias("std_val")).first()
percentiles = df.select("valuation").rdd.map(lambda x: x[0]).collect()
percentiles_np = np.array(percentiles)
p25, p50, p75, p90, p95 = np.percentile(percentiles_np, [25, 50, 75, 90, 95])
range_distribution = df.withColumn("valuation_range", when(col("valuation") < 1000000000, "< 1B").when((col("valuation") >= 1000000000) & (col("valuation") < 5000000000), "1B-5B").when((col("valuation") >= 5000000000) & (col("valuation") < 10000000000), "5B-10B").when((col("valuation") >= 10000000000) & (col("valuation") < 50000000000), "10B-50B").otherwise("> 50B")).groupBy("valuation_range").agg(count("company_id").alias("count"), avg("valuation").alias("avg_valuation")).orderBy("avg_valuation")
industry_valuation = df.groupBy("industry").agg(count("company_id").alias("count"), sum("valuation").alias("total_valuation"), avg("valuation").alias("avg_valuation"), stddev("valuation").alias("std_valuation")).orderBy(desc("avg_valuation"))
valuation_growth = df.select("company_id", "industry", ((col("valuation_current") - col("valuation_previous")) / col("valuation_previous") * 100).alias("valuation_growth")).filter(col("valuation_growth").isNotNull())
growth_distribution = valuation_growth.withColumn("growth_range", when(col("valuation_growth") < -20, "< -20%").when((col("valuation_growth") >= -20) & (col("valuation_growth") < 0), "-20% to 0%").when((col("valuation_growth") >= 0) & (col("valuation_growth") < 20), "0% to 20%").when((col("valuation_growth") >= 20) & (col("valuation_growth") < 50), "20% to 50%").otherwise("> 50%")).groupBy("growth_range").agg(count("company_id").alias("count"))
correlation_analysis = df.select("valuation", "revenue", "employee_count").toPandas().corr()['valuation'].to_dict()
range_result = range_distribution.toPandas().to_dict('records')
industry_result = industry_valuation.toPandas().to_dict('records')
result = {"basic_stats": {"min": float(valuation_stats.min_val), "max": float(valuation_stats.max_val), "avg": float(valuation_stats.avg_val), "std": float(valuation_stats.std_val)}, "percentiles": {"p25": float(p25), "p50": float(p50), "p75": float(p75), "p90": float(p90), "p95": float(p95)}, "range_distribution": range_result, "industry_distribution": industry_result}
return JsonResponse(result)
基于大数据的胡润榜全球企业估值分析系统文档展示
💖💖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
