前言
- 💖💖作者：计算机程序员小杨
- 💙💙个人简介：我是一名计算机相关专业的从业者，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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！
- 💕💕文末获取源码联系 计算机程序员小杨
- 💜💜
- 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
- 计算机毕业设计选题
- 💜💜
一.开发工具简介
- 大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
- 开发语言：Python+Java（两个版本都支持）
- 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
- 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
- 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
- 数据库：MySQL
二.系统内容简介
基于大数据的国家医用消耗选品采集数据可视化分析系统是一套综合运用Hadoop、Spark等大数据技术构建的专业化数据分析平台。系统采用Python语言开发，后端基于Django框架搭建，前端运用Vue+ElementUI+Echarts技术栈实现用户界面和数据可视化展示。系统核心功能涵盖系统首页、用户信息管理、医用消耗选品信息管理、大屏可视化展示、市场竞争格局分析、耗材价格多维分析、产品技术特性分析以及人工关节专题分析等九大模块。通过Spark SQL进行大数据查询处理，结合Pandas、NumPy等数据分析库实现复杂的统计分析功能，利用HDFS分布式存储海量医用消耗品数据，最终通过Echarts图表库将分析结果以直观的可视化形式呈现给用户。系统能够有效处理国家医用消耗选品的海量数据，为相关决策提供数据支撑，同时展现了现代大数据技术在医疗健康领域的实际应用价值。
三.系统功能演示
毕设没亮点？基于Spark+Echarts的国家医用消耗选品数据可视化分析系统帮你解决
四.系统界面展示
五.系统源码展示
from pyspark.sql import SparkSession
from django.http import JsonResponse
from django.views import View
import pandas as pd
import numpy as np
from collections import Counter
import json
def medical_supply_data_analysis():
spark = SparkSession.builder.appName("MedicalSupplyAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.read.format("csv").option("header", "true").load("hdfs://localhost:9000/medical_supply_data/*.csv")
df.createOrReplaceTempView("medical_supplies")
analysis_result = spark.sql("""
SELECT supply_category, supplier_name,
AVG(unit_price) as avg_price,
COUNT(*) as product_count,
SUM(purchase_volume) as total_volume,
STDDEV(unit_price) as price_deviation
FROM medical_supplies
WHERE supply_status = 'active' AND unit_price > 0
GROUP BY supply_category, supplier_name
HAVING COUNT(*) >= 5
ORDER BY total_volume DESC
""")
pandas_df = analysis_result.toPandas()
category_stats = pandas_df.groupby('supply_category').agg({
'avg_price': ['mean', 'std', 'min', 'max'],
'product_count': 'sum',
'total_volume': 'sum'
}).round(2)
market_concentration = pandas_df.groupby('supply_category')['total_volume'].apply(
lambda x: (x.nlargest(3).sum() / x.sum()) * 100
).round(2)
price_analysis = {}
for category in pandas_df['supply_category'].unique():
category_data = pandas_df[pandas_df['supply_category'] == category]
price_range = category_data['avg_price'].quantile([0.25, 0.5, 0.75]).tolist()
coefficient_variation = (category_data['avg_price'].std() / category_data['avg_price'].mean()) * 100
price_analysis[category] = {
'quartiles': price_range,
'variation_coefficient': round(coefficient_variation, 2),
'supplier_count': len(category_data)
}
return {
'category_statistics': category_stats.to_dict(),
'market_concentration': market_concentration.to_dict(),
'price_analysis': price_analysis,
'total_categories': len(pandas_df['supply_category'].unique())
}
def competition_pattern_analysis():
spark = SparkSession.builder.appName("CompetitionAnalysis").getOrCreate()
supply_df = spark.read.format("csv").option("header", "true").load("hdfs://localhost:9000/competition_data/*.csv")
supply_df.createOrReplaceTempView("competition_data")
competition_query = spark.sql("""
SELECT supplier_name, supply_category,
COUNT(DISTINCT product_name) as product_variety,
SUM(market_share) as total_market_share,
AVG(customer_satisfaction) as avg_satisfaction,
MAX(technology_level) as max_tech_level,
MIN(unit_price) as min_price,
MAX(unit_price) as max_price
FROM competition_data
WHERE market_share > 0 AND customer_satisfaction >= 3.0
GROUP BY supplier_name, supply_category
ORDER BY total_market_share DESC
""")
competition_df = competition_query.toPandas()
market_leaders = competition_df.groupby('supply_category').head(3)
competitive_metrics = {}
for category in competition_df['supply_category'].unique():
category_competitors = competition_df[competition_df['supply_category'] == category]
hhi_index = ((category_competitors['total_market_share'] ** 2).sum()) / 10000
avg_products_per_supplier = category_competitors['product_variety'].mean()
price_competition_intensity = category_competitors['max_price'].std() / category_competitors['max_price'].mean()
tech_advancement = (category_competitors['max_tech_level'] >= 8).sum() / len(category_competitors)
competitive_metrics[category] = {
'hhi_concentration': round(hhi_index, 3),
'avg_product_diversity': round(avg_products_per_supplier, 1),
'price_volatility': round(price_competition_intensity, 3),
'high_tech_ratio': round(tech_advancement, 2),
'top_suppliers': category_competitors.head(3)[['supplier_name', 'total_market_share']].to_dict('records')
}
overall_competition = {
'total_suppliers': len(competition_df['supplier_name'].unique()),
'average_satisfaction': round(competition_df['avg_satisfaction'].mean(), 2),
'categories_analyzed': len(competitive_metrics)
}
return {
'competitive_metrics': competitive_metrics,
'market_leaders': market_leaders.to_dict('records'),
'overall_statistics': overall_competition
}
def artificial_joint_analysis():
spark = SparkSession.builder.appName("JointAnalysis").getOrCreate()
joint_df = spark.read.format("csv").option("header", "true").load("hdfs://localhost:9000/joint_data/*.csv")
joint_df.createOrReplaceTempView("joint_products")
joint_analysis_sql = spark.sql("""
SELECT joint_type, material_type, manufacturer,
COUNT(*) as product_count,
AVG(durability_years) as avg_durability,
AVG(biocompatibility_score) as avg_biocompat,
SUM(clinical_cases) as total_cases,
AVG(success_rate) as avg_success_rate,
AVG(unit_cost) as avg_cost
FROM joint_products
WHERE approval_status = 'approved' AND clinical_cases > 100
GROUP BY joint_type, material_type, manufacturer
HAVING AVG(success_rate) >= 0.85
ORDER BY avg_success_rate DESC, total_cases DESC
""")
joint_pandas = joint_analysis_sql.toPandas()
material_performance = joint_pandas.groupby('material_type').agg({
'avg_durability': 'mean',
'avg_biocompat': 'mean',
'avg_success_rate': 'mean',
'total_cases': 'sum'
}).round(3)
joint_type_analysis = {}
for joint_type in joint_pandas['joint_type'].unique():
type_data = joint_pandas[joint_pandas['joint_type'] == joint_type]
best_materials = type_data.nlargest(3, 'avg_success_rate')[['material_type', 'avg_success_rate', 'avg_cost']]
cost_effectiveness = type_data['avg_success_rate'] / (type_data['avg_cost'] / 1000)
joint_type_analysis[joint_type] = {
'recommended_materials': best_materials.to_dict('records'),
'avg_cost_range': [type_data['avg_cost'].min(), type_data['avg_cost'].max()],
'clinical_evidence': type_data['total_cases'].sum(),
'cost_effectiveness_leader': type_data.loc[cost_effectiveness.idxmax(), 'manufacturer']
}
innovation_trends = joint_pandas.groupby('manufacturer')['avg_biocompat'].mean().nlargest(5)
durability_leaders = joint_pandas.nlargest(5, 'avg_durability')[['manufacturer', 'joint_type', 'avg_durability']]
return {
'material_performance': material_performance.to_dict(),
'joint_type_analysis': joint_type_analysis,
'innovation_trends': innovation_trends.to_dict(),
'durability_leaders': durability_leaders.to_dict('records'),
'analysis_summary': {
'total_products_analyzed': len(joint_pandas),
'materials_evaluated': len(joint_pandas['material_type'].unique()),
'manufacturers_included': len(joint_pandas['manufacturer'].unique())
}
}
