💖💖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
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国家医用消耗选品采集数据可视化分析系统介绍
《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》是一个集数据采集、存储、分析与可视化于一体的综合性大数据应用系统，专门针对国家医用消耗品选品决策提供数据支撑和分析服务。该系统采用先进的大数据技术架构，以Hadoop分布式文件系统（HDFS）作为底层数据存储基础，利用Spark大数据处理引擎进行高效的数据计算和分析，通过Spark SQL实现复杂的数据查询操作，结合Pandas和NumPy等Python科学计算库进行精确的数据处理。系统后端基于Django框架构建RESTful API接口，前端采用Vue.js框架结合ElementUI组件库打造现代化的用户界面，通过Echarts图表库实现丰富的数据可视化效果。系统核心功能包括用户权限管理、个人信息维护、医用消耗选品信息的全生命周期管理，以及强大的大屏可视化展示功能。特别是在数据分析层面，系统提供市场竞争格局深度分析，能够从多个维度剖析耗材价格变化趋势，深入挖掘产品技术特性数据，并专门针对人工关节等重点医用消耗品类别提供专题分析报告，为国家医用消耗品采购决策、价格监管、质量评估提供科学的数据依据和决策支持，有效提升医疗资源配置效率和医用消耗品管理水平。
国家医用消耗选品采集数据可视化分析系统演示视频
国家医用消耗选品采集数据可视化分析系统演示图片
国家医用消耗选品采集数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, max, min, when, desc, asc
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, IntegerType
import pandas as pd
from django.http import JsonResponse
from django.views.decorators.http import require_http_methods
import json
spark = SparkSession.builder.appName("MedicalConsumablesAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@require_http_methods(["POST"])
def market_competition_analysis(request):
data = json.loads(request.body)
category = data.get('category', '')
time_range = data.get('time_range', 30)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/medical_db").option("dbtable", "consumables_info").option("user", "root").option("password", "123456").load()
filtered_df = df.filter(col("category").like(f"%{category}%"))
if time_range:
filtered_df = filtered_df.filter(col("update_date") >= f"DATE_SUB(NOW(), INTERVAL {time_range} DAY)")
market_share = filtered_df.groupBy("manufacturer").agg(count("*").alias("product_count"), sum("sales_volume").alias("total_sales"), avg("price").alias("avg_price")).orderBy(desc("total_sales"))
price_analysis = filtered_df.groupBy("manufacturer").agg(avg("price").alias("avg_price"), min("price").alias("min_price"), max("price").alias("max_price"))
quality_stats = filtered_df.groupBy("manufacturer").agg(avg("quality_score").alias("avg_quality"), count(when(col("certification_status") == "已认证", True)).alias("certified_count"))
market_data = market_share.join(price_analysis, "manufacturer").join(quality_stats, "manufacturer")
total_sales = market_data.agg(sum("total_sales")).collect()[0][0]
market_final = market_data.withColumn("market_share_percent", (col("total_sales") / total_sales * 100).cast("decimal(10,2)"))
competition_index = market_final.withColumn("competition_score", (col("market_share_percent") * 0.4 + col("avg_quality") * 0.3 + (100 - col("avg_price") / col("avg_price").over()) * 0.3))
result_data = competition_index.select("manufacturer", "product_count", "total_sales", "market_share_percent", "avg_price", "avg_quality", "competition_score").orderBy(desc("competition_score")).collect()
manufacturers = [row["manufacturer"] for row in result_data]
market_shares = [float(row["market_share_percent"]) for row in result_data]
competition_scores = [float(row["competition_score"]) for row in result_data]
return JsonResponse({"manufacturers": manufacturers, "market_shares": market_shares, "competition_scores": competition_scores, "analysis_summary": {"total_manufacturers": len(manufacturers), "market_concentration": sum(market_shares[:3]), "avg_competition_score": sum(competition_scores) / len(competition_scores)}})
@require_http_methods(["POST"])
def multi_dimensional_price_analysis(request):
data = json.loads(request.body)
product_type = data.get('product_type', '')
analysis_dimension = data.get('dimension', 'time')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/medical_db").option("dbtable", "price_history").option("user", "root").option("password", "123456").load()
if product_type:
df = df.filter(col("product_type").like(f"%{product_type}%"))
if analysis_dimension == 'time':
time_analysis = df.groupBy("price_date").agg(avg("price").alias("avg_price"), min("price").alias("min_price"), max("price").alias("max_price"), count("*").alias("record_count")).orderBy("price_date")
price_trend = time_analysis.select("price_date", "avg_price").collect()
dates = [row["price_date"].strftime("%Y-%m-%d") for row in price_trend]
prices = [float(row["avg_price"]) for row in price_trend]
volatility = time_analysis.select(((col("max_price") - col("min_price")) / col("avg_price") * 100).alias("daily_volatility")).agg(avg("daily_volatility")).collect()[0][0]
elif analysis_dimension == 'region':
region_analysis = df.groupBy("region").agg(avg("price").alias("avg_price"), count("*").alias("sample_count"), min("price").alias("min_price"), max("price").alias("max_price")).orderBy("avg_price")
region_data = region_analysis.collect()
dates = [row["region"] for row in region_data]
prices = [float(row["avg_price"]) for row in region_data]
price_gap = region_analysis.agg((max("avg_price") - min("avg_price")) / avg("avg_price") * 100).collect()[0][0]
volatility = float(price_gap)
elif analysis_dimension == 'specification':
spec_analysis = df.groupBy("specification").agg(avg("price").alias("avg_price"), count("*").alias("sample_count")).orderBy(desc("avg_price"))
spec_data = spec_analysis.collect()
dates = [row["specification"] for row in spec_data]
prices = [float(row["avg_price"]) for row in spec_data]
volatility = spec_analysis.select(((col("avg_price") - avg("avg_price").over()) / avg("avg_price").over() * 100).alias("price_deviation")).agg(avg("price_deviation")).collect()[0][0]
price_correlation = df.select("price", "quality_score", "market_demand").toPandas().corr()["price"].to_dict()
return JsonResponse({"dimension": analysis_dimension, "labels": dates, "price_data": prices, "volatility_index": round(float(volatility), 2), "correlation_analysis": {"quality_correlation": round(price_correlation.get("quality_score", 0), 3), "demand_correlation": round(price_correlation.get("market_demand", 0), 3)}, "price_summary": {"avg_price": round(sum(prices) / len(prices), 2), "max_price": round(max(prices), 2), "min_price": round(min(prices), 2)}})
@require_http_methods(["POST"])
def artificial_joint_special_analysis(request):
data = json.loads(request.body)
joint_type = data.get('joint_type', 'all')
analysis_scope = data.get('scope', 'comprehensive')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/medical_db").option("dbtable", "artificial_joints").option("user", "root").option("password", "123456").load()
if joint_type != 'all':
df = df.filter(col("joint_type") == joint_type)
material_analysis = df.groupBy("material_type").agg(count("*").alias("product_count"), avg("durability_years").alias("avg_durability"), avg("biocompatibility_score").alias("avg_biocompatibility"), avg("price").alias("avg_price")).orderBy(desc("avg_biocompatibility"))
technology_analysis = df.groupBy("manufacturing_tech").agg(count("*").alias("tech_count"), avg("precision_level").alias("avg_precision"), avg("success_rate").alias("avg_success_rate")).orderBy(desc("avg_success_rate"))
performance_metrics = df.select("joint_type", "durability_years", "biocompatibility_score", "wear_resistance", "corrosion_resistance", "price").groupBy("joint_type").agg(avg("durability_years").alias("avg_durability"), avg("biocompatibility_score").alias("avg_biocompatibility"), avg("wear_resistance").alias("avg_wear_resistance"), avg("corrosion_resistance").alias("avg_corrosion_resistance"), avg("price").alias("avg_price"))
supplier_evaluation = df.groupBy("supplier_name").agg(count("*").alias("product_variety"), avg("quality_certification_level").alias("avg_cert_level"), avg("delivery_time").alias("avg_delivery_time"), sum("annual_sales_volume").alias("total_sales")).orderBy(desc("avg_cert_level"))
cost_effectiveness = df.select("joint_type", ((col("durability_years") * col("biocompatibility_score")) / col("price")).alias("cost_effectiveness_ratio")).groupBy("joint_type").agg(avg("cost_effectiveness_ratio").alias("avg_cost_effectiveness")).orderBy(desc("avg_cost_effectiveness"))
material_data = material_analysis.collect()
tech_data = technology_analysis.collect()
performance_data = performance_metrics.collect()
supplier_data = supplier_evaluation.collect()
cost_data = cost_effectiveness.collect()
innovation_trend = df.filter(col("release_date") >= "2023-01-01").groupBy("innovation_level").count().orderBy(desc("count")).collect()
market_penetration = df.groupBy("market_segment").agg(sum("installation_cases").alias("total_cases"), avg("patient_satisfaction").alias("avg_satisfaction")).orderBy(desc("total_cases")).collect()
return JsonResponse({"material_analysis": [{"material": row["material_type"], "count": row["product_count"], "durability": float(row["avg_durability"]), "biocompatibility": float(row["avg_biocompatibility"]), "price": float(row["avg_price"])} for row in material_data], "technology_trends": [{"technology": row["manufacturing_tech"], "count": row["tech_count"], "precision": float(row["avg_precision"]), "success_rate": float(row["avg_success_rate"])} for row in tech_data], "performance_comparison": [{"joint_type": row["joint_type"], "durability": float(row["avg_durability"]), "biocompatibility": float(row["avg_biocompatibility"]), "wear_resistance": float(row["avg_wear_resistance"]), "price": float(row["avg_price"])} for row in performance_data], "supplier_ranking": [{"supplier": row["supplier_name"], "variety": row["product_variety"], "cert_level": float(row["avg_cert_level"]), "delivery_time": float(row["avg_delivery_time"]), "sales": row["total_sales"]} for row in supplier_data], "cost_effectiveness": [{"joint_type": row["joint_type"], "ratio": float(row["avg_cost_effectiveness"])} for row in cost_data], "innovation_summary": {"trend_data": [{"level": row["innovation_level"], "count": row["count"]} for row in innovation_trend], "market_penetration": [{"segment": row["market_segment"], "cases": row["total_cases"], "satisfaction": float(row["avg_satisfaction"])} for row in market_penetration]}})
国家医用消耗选品采集数据可视化分析系统文档展示
💖💖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
