💖💖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
基于大数据的红斑鳞状疾病数据可视化分析系统介绍
基于大数据的红斑鳞状疾病数据可视化分析系统是一套专门针对红斑鳞状疾病相关数据进行深度分析和可视化展示的综合性平台。该系统采用当前主流的大数据技术架构，通过Hadoop分布式文件系统HDFS实现海量医疗数据的存储管理，利用Spark大数据处理引擎进行高效的数据清洗、转换和分析计算。在技术实现上，系统提供Python+Django和Java+Spring Boot两套完整的后端解决方案，前端采用Vue.js结合ElementUI组件库构建用户界面，通过Echarts图表库实现丰富的数据可视化效果。系统核心功能包括完善的系统管理模块和强大的数据可视化分析模块，能够处理红斑鳞状疾病的病例数据、治疗记录、症状分布等多维度信息。通过Spark SQL进行复杂的数据查询和统计分析，结合Pandas和NumPy进行数据科学计算，最终以直观的图表形式展现疾病发展趋势、地域分布特征、治疗效果对比等关键指标，为医疗研究人员和临床医生提供有价值的数据洞察支持。
基于大数据的红斑鳞状疾病数据可视化分析系统演示视频
基于大数据的红斑鳞状疾病数据可视化分析系统演示图片
基于大数据的红斑鳞状疾病数据可视化分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import mysql.connector
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("DiseaseAnalysisSystem").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def disease_data_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
start_date = data.get('start_date')
end_date = data.get('end_date')
disease_type = data.get('disease_type', 'all')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("dbtable", "disease_records").option("user", "root").option("password", "password").load()
filtered_df = df.filter((col("record_date") >= start_date) & (col("record_date") <= end_date))
if disease_type != 'all':
filtered_df = filtered_df.filter(col("disease_type") == disease_type)
age_distribution = filtered_df.groupBy("age_group").agg(count("*").alias("patient_count"), avg("severity_score").alias("avg_severity")).orderBy("age_group")
gender_stats = filtered_df.groupBy("gender").agg(count("*").alias("count"), avg("treatment_duration").alias("avg_duration"))
monthly_trend = filtered_df.withColumn("month", date_format(col("record_date"), "yyyy-MM")).groupBy("month").agg(count("*").alias("case_count"), avg("recovery_rate").alias("avg_recovery"))
severity_analysis = filtered_df.groupBy("severity_level").agg(count("*").alias("count"), avg("hospital_stay").alias("avg_stay"))
treatment_effectiveness = filtered_df.groupBy("treatment_method").agg(count("*").alias("total_cases"), sum(when(col("treatment_result") == "improved", 1).otherwise(0)).alias("success_cases"))
treatment_effectiveness = treatment_effectiveness.withColumn("success_rate", col("success_cases") / col("total_cases") * 100)
result_data = {
'age_distribution': age_distribution.collect(),
'gender_stats': gender_stats.collect(),
'monthly_trend': monthly_trend.orderBy("month").collect(),
'severity_analysis': severity_analysis.collect(),
'treatment_effectiveness': treatment_effectiveness.collect()
}
return JsonResponse({'status': 'success', 'data': result_data})
@csrf_exempt
def system_user_management(request):
if request.method == 'POST':
action = request.POST.get('action')
if action == 'create_user':
username = request.POST.get('username')
password = request.POST.get('password')
email = request.POST.get('email')
role = request.POST.get('role', 'user')
department = request.POST.get('department')
conn = mysql.connector.connect(host='localhost', user='root', password='password', database='disease_db')
cursor = conn.cursor()
check_query = "SELECT COUNT(*) FROM users WHERE username = %s OR email = %s"
cursor.execute(check_query, (username, email))
if cursor.fetchone()[0] > 0:
return JsonResponse({'status': 'error', 'message': 'Username or email already exists'})
hashed_password = hash(password)
insert_query = "INSERT INTO users (username, password, email, role, department, create_time, status) VALUES (%s, %s, %s, %s, %s, %s, %s)"
cursor.execute(insert_query, (username, str(hashed_password), email, role, department, datetime.now(), 'active'))
user_id = cursor.lastrowid
log_query = "INSERT INTO user_logs (user_id, action, details, timestamp) VALUES (%s, %s, %s, %s)"
cursor.execute(log_query, (user_id, 'user_created', f'New user {username} created', datetime.now()))
conn.commit()
cursor.close()
conn.close()
return JsonResponse({'status': 'success', 'message': 'User created successfully', 'user_id': user_id})
elif action == 'update_permissions':
user_id = request.POST.get('user_id')
permissions = json.loads(request.POST.get('permissions'))
conn = mysql.connector.connect(host='localhost', user='root', password='password', database='disease_db')
cursor = conn.cursor()
delete_query = "DELETE FROM user_permissions WHERE user_id = %s"
cursor.execute(delete_query, (user_id,))
for permission in permissions:
insert_query = "INSERT INTO user_permissions (user_id, module_name, permission_type, granted_by, grant_time) VALUES (%s, %s, %s, %s, %s)"
cursor.execute(insert_query, (user_id, permission['module'], permission['type'], request.user.id, datetime.now()))
conn.commit()
cursor.close()
conn.close()
return JsonResponse({'status': 'success', 'message': 'Permissions updated successfully'})
@csrf_exempt
def disease_visualization_data(request):
if request.method == 'GET':
chart_type = request.GET.get('chart_type')
time_range = request.GET.get('time_range', '30')
end_date = datetime.now()
start_date = end_date - timedelta(days=int(time_range))
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("dbtable", "disease_records").option("user", "root").option("password", "password").load()
filtered_df = df.filter((col("record_date") >= start_date.strftime('%Y-%m-%d')) & (col("record_date") <= end_date.strftime('%Y-%m-%d')))
if chart_type == 'disease_distribution':
disease_stats = filtered_df.groupBy("disease_type").agg(count("*").alias("count"), avg("severity_score").alias("avg_score"))
chart_data = [{'name': row['disease_type'], 'value': row['count'], 'avg_score': float(row['avg_score'])} for row in disease_stats.collect()]
elif chart_type == 'regional_analysis':
regional_data = filtered_df.groupBy("region", "disease_type").agg(count("*").alias("case_count"), avg("recovery_rate").alias("recovery_rate"))
pivot_data = regional_data.groupBy("region").pivot("disease_type").agg(sum("case_count").alias("count"))
chart_data = [{'region': row['region'], 'data': [row[col] if row[col] is not None else 0 for col in pivot_data.columns[1:]]} for row in pivot_data.collect()]
elif chart_type == 'treatment_timeline':
timeline_data = filtered_df.withColumn("week", date_format(col("record_date"), "yyyy-ww")).groupBy("week", "treatment_method").agg(count("*").alias("count"), avg("treatment_duration").alias("avg_duration"))
timeline_result = timeline_data.orderBy("week").collect()
chart_data = []
for row in timeline_result:
chart_data.append({'week': row['week'], 'method': row['treatment_method'], 'count': row['count'], 'duration': float(row['avg_duration'])})
elif chart_type == 'symptom_correlation':
symptom_df = filtered_df.select("symptom1", "symptom2", "symptom3", "severity_score", "treatment_result")
correlation_matrix = []
symptoms = ['symptom1', 'symptom2', 'symptom3']
for i, sym1 in enumerate(symptoms):
for j, sym2 in enumerate(symptoms):
if i <= j:
corr_val = symptom_df.stat.corr(sym1, sym2) if i != j else 1.0
correlation_matrix.append({'x': i, 'y': j, 'value': round(corr_val, 3)})
chart_data = correlation_matrix
spark.catalog.clearCache()
return JsonResponse({'status': 'success', 'chart_data': chart_data, 'data_count': filtered_df.count()})
基于大数据的红斑鳞状疾病数据可视化分析系统文档展示
💖💖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
