💖💖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
基于大数据的深圳市养老机构信息分析系统介绍
《养老机构信息分析系统》是一个基于大数据技术栈构建的综合性信息管理与分析平台，采用Hadoop+Spark分布式计算框架作为数据处理核心，结合Python语言的强大数据分析能力和Django Web框架的稳定性，构建了完整的后端服务体系。系统前端采用Vue.js框架配合ElementUI组件库，通过Echarts图表库实现数据的直观可视化展示，为用户提供流畅的交互体验。系统核心功能涵盖养老机构基础信息管理、多维度数据可视化大屏展示、基于机器学习的热点聚类分析、服务能力基准评估分析、地理信息系统支持的空间分布分析以及机构类型性质的深度统计分析。通过Spark SQL进行高效的数据查询处理，利用Pandas和NumPy进行复杂的数据清洗与特征工程，实现了从数据采集、存储、处理到分析展示的完整业务闭环。系统采用HDFS分布式文件系统确保数据存储的可靠性和扩展性，MySQL数据库承担结构化数据的持久化存储任务，整体架构充分体现了现代大数据技术在实际业务场景中的应用价值。
基于大数据的深圳市养老机构信息分析系统演示视频
基于大数据的深圳市养老机构信息分析系统演示图片
基于大数据的深圳市养老机构信息分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, desc, asc
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
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("ElderCareAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def institution_clustering_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
cluster_num = data.get('cluster_num', 5)
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eldercare_db").option("dbtable", "institution_info").option("user", "root").option("password", "password").load()
numeric_features = ['bed_count', 'nurse_count', 'service_score', 'facility_score', 'monthly_fee']
assembler = VectorAssembler(inputCols=numeric_features, outputCol="features")
feature_df = assembler.transform(df)
kmeans = KMeans(k=cluster_num, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(feature_df)
predictions = model.transform(feature_df)
cluster_centers = model.clusterCenters()
cluster_stats = predictions.groupBy("cluster").agg(
count("*").alias("count"),
avg("bed_count").alias("avg_beds"),
avg("nurse_count").alias("avg_nurses"),
avg("service_score").alias("avg_service"),
avg("monthly_fee").alias("avg_fee")
).collect()
result_data = []
for i, row in enumerate(cluster_stats):
cluster_info = {
'cluster_id': row['cluster'],
'institution_count': row['count'],
'average_beds': round(row['avg_beds'], 2),
'average_nurses': round(row['avg_nurses'], 2),
'average_service_score': round(row['avg_service'], 2),
'average_monthly_fee': round(row['avg_fee'], 2),
'cluster_center': cluster_centers[i].tolist()
}
result_data.append(cluster_info)
institution_details = predictions.select("institution_name", "cluster", "bed_count", "nurse_count", "service_score", "monthly_fee").collect()
detailed_results = []
for row in institution_details:
institution_detail = {
'name': row['institution_name'],
'cluster': row['cluster'],
'beds': row['bed_count'],
'nurses': row['nurse_count'],
'score': row['service_score'],
'fee': row['monthly_fee']
}
detailed_results.append(institution_detail)
response_data = {
'cluster_summary': result_data,
'institution_details': detailed_results,
'total_clusters': cluster_num,
'analysis_status': 'success'
}
return JsonResponse(response_data, safe=False)
@csrf_exempt
def service_capacity_benchmark(request):
if request.method == 'POST':
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eldercare_db").option("dbtable", "institution_service").option("user", "root").option("password", "password").load()
service_metrics = df.select("institution_id", "medical_service", "daily_care", "recreation_activity", "psychological_support", "rehabilitation_service").collect()
benchmark_data = []
for row in service_metrics:
institution_services = {
'medical': row['medical_service'],
'daily_care': row['daily_care'],
'recreation': row['recreation_activity'],
'psychological': row['psychological_support'],
'rehabilitation': row['rehabilitation_service']
}
service_scores = list(institution_services.values())
total_score = sum(service_scores)
average_score = total_score / len(service_scores)
service_balance = np.std(service_scores)
benchmark_result = {
'institution_id': row['institution_id'],
'total_service_score': total_score,
'average_service_score': round(average_score, 2),
'service_balance_index': round(service_balance, 2),
'service_details': institution_services
}
if total_score >= 40:
benchmark_result['service_level'] = '优秀'
elif total_score >= 30:
benchmark_result['service_level'] = '良好'
elif total_score >= 20:
benchmark_result['service_level'] = '合格'
else:
benchmark_result['service_level'] = '待改进'
benchmark_data.append(benchmark_result)
overall_stats = df.agg(
avg("medical_service").alias("avg_medical"),
avg("daily_care").alias("avg_daily"),
avg("recreation_activity").alias("avg_recreation"),
avg("psychological_support").alias("avg_psychological"),
avg("rehabilitation_service").alias("avg_rehabilitation")
).collect()[0]
industry_benchmark = {
'medical_benchmark': round(overall_stats['avg_medical'], 2),
'daily_care_benchmark': round(overall_stats['avg_daily'], 2),
'recreation_benchmark': round(overall_stats['avg_recreation'], 2),
'psychological_benchmark': round(overall_stats['avg_psychological'], 2),
'rehabilitation_benchmark': round(overall_stats['avg_rehabilitation'], 2)
}
response_data = {
'institution_benchmarks': benchmark_data,
'industry_standards': industry_benchmark,
'analysis_timestamp': pd.Timestamp.now().strftime('%Y-%m-%d %H:%M:%S')
}
return JsonResponse(response_data, safe=False)
@csrf_exempt
def spatial_distribution_analysis(request):
if request.method == 'POST':
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/eldercare_db").option("dbtable", "institution_location").option("user", "root").option("password", "password").load()
location_data = df.select("institution_id", "province", "city", "district", "latitude", "longitude", "bed_count", "service_radius").collect()
province_distribution = df.groupBy("province").agg(
count("*").alias("institution_count"),
sum("bed_count").alias("total_beds"),
avg("service_radius").alias("avg_radius")
).orderBy(desc("institution_count")).collect()
city_distribution = df.groupBy("province", "city").agg(
count("*").alias("city_institution_count"),
sum("bed_count").alias("city_total_beds"),
avg("latitude").alias("avg_latitude"),
avg("longitude").alias("avg_longitude")
).orderBy(desc("city_institution_count")).collect()
spatial_analysis_results = []
for row in location_data:
location_info = {
'institution_id': row['institution_id'],
'province': row['province'],
'city': row['city'],
'district': row['district'],
'coordinates': {
'lat': float(row['latitude']),
'lng': float(row['longitude'])
},
'bed_capacity': row['bed_count'],
'service_radius': row['service_radius']
}
spatial_analysis_results.append(location_info)
province_stats = []
for row in province_distribution:
province_info = {
'province_name': row['province'],
'institution_count': row['institution_count'],
'total_bed_capacity': row['total_beds'],
'average_service_radius': round(row['avg_radius'], 2),
'beds_per_institution': round(row['total_beds'] / row['institution_count'], 2)
}
province_stats.append(province_info)
city_stats = []
for row in city_distribution:
city_info = {
'province': row['province'],
'city_name': row['city'],
'institution_count': row['city_institution_count'],
'total_beds': row['city_total_beds'],
'city_center': {
'lat': round(row['avg_latitude'], 6),
'lng': round(row['avg_longitude'], 6)
}
}
city_stats.append(city_info)
response_data = {
'spatial_distribution': spatial_analysis_results,
'province_statistics': province_stats,
'city_statistics': city_stats,
'total_institutions': len(location_data),
'analysis_scope': 'nationwide'
}
return JsonResponse(response_data, safe=False)
基于大数据的深圳市养老机构信息分析系统文档展示
💖💖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
