💖💖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
基于大数据的气候驱动的疾病传播系统介绍
气候驱动的疾病传播系统是一个基于大数据技术的综合性分析平台，采用Hadoop+Spark分布式计算框架作为核心技术架构，通过Python开发语言结合Django后端框架构建稳定的服务层，前端运用Vue+ElementUI+Echarts技术栈打造直观的数据可视化界面。系统核心功能涵盖帕金森病数据管理、数据集总体分析、语音声学特征分析、多维特征关联分析以及非线性动力学分析等七大模块，能够处理大规模医疗数据并进行深度挖掘。平台利用HDFS分布式文件系统存储海量气候与疾病相关数据，通过Spark SQL进行高效查询分析，结合Pandas和NumPy进行科学计算，实现对气候因素与疾病传播规律的智能化分析。系统不仅支持多维度数据关联分析，还能通过先进的声学特征提取技术对帕金森病患者语音数据进行精准分析，为医疗研究人员提供科学的数据支撑和决策参考。
基于大数据的气候驱动的疾病传播系统演示视频
基于大数据的气候驱动的疾病传播系统演示图片
基于大数据的气候驱动的疾病传播系统代码展示
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
import json
spark = SparkSession.builder.appName("ClimateDiseasePrediction").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def parkinson_data_management(request):
if request.method == 'POST':
data = json.loads(request.body)
patient_id = data.get('patient_id')
voice_features = data.get('voice_features')
climate_data = data.get('climate_data')
diagnosis_info = data.get('diagnosis_info')
parkinson_df = spark.createDataFrame([
(patient_id, voice_features['jitter'], voice_features['shimmer'],
voice_features['nhr'], climate_data['temperature'],
climate_data['humidity'], diagnosis_info['stage'])
], ["patient_id", "jitter", "shimmer", "nhr", "temperature", "humidity", "stage"])
parkinson_df.write.mode("append").option("header", "true").csv("hdfs://localhost:9000/parkinson_data/")
existing_data = spark.read.option("header", "true").csv("hdfs://localhost:9000/parkinson_data/")
patient_count = existing_data.count()
stage_distribution = existing_data.groupBy("stage").count().collect()
feature_stats = existing_data.select(
avg("jitter").alias("avg_jitter"),
avg("shimmer").alias("avg_shimmer"),
avg("nhr").alias("avg_nhr"),
stddev("temperature").alias("temp_std")
).collect()[0]
correlation_matrix = existing_data.select("jitter", "shimmer", "temperature", "humidity").toPandas().corr()
return JsonResponse({
'status': 'success',
'patient_count': patient_count,
'stage_distribution': [{'stage': row['stage'], 'count': row['count']} for row in stage_distribution],
'feature_statistics': {
'avg_jitter': float(feature_stats['avg_jitter']),
'avg_shimmer': float(feature_stats['avg_shimmer']),
'avg_nhr': float(feature_stats['avg_nhr']),
'temp_std': float(feature_stats['temp_std'])
},
'correlations': correlation_matrix.to_dict()
})
def voice_acoustic_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
audio_file_path = data.get('audio_path')
patient_id = data.get('patient_id')
analysis_type = data.get('analysis_type', 'comprehensive')
voice_data = spark.read.option("header", "true").csv("hdfs://localhost:9000/voice_features/")
patient_voice_data = voice_data.filter(voice_data.patient_id == patient_id)
jitter_analysis = patient_voice_data.select(
mean("jitter").alias("mean_jitter"),
stddev("jitter").alias("std_jitter"),
min("jitter").alias("min_jitter"),
max("jitter").alias("max_jitter")
).collect()[0]
shimmer_analysis = patient_voice_data.select(
mean("shimmer").alias("mean_shimmer"),
stddev("shimmer").alias("std_shimmer"),
min("shimmer").alias("min_shimmer"),
max("shimmer").alias("max_shimmer")
).collect()[0]
nhr_analysis = patient_voice_data.select(
mean("nhr").alias("mean_nhr"),
stddev("nhr").alias("std_nhr"),
min("nhr").alias("min_nhr"),
max("nhr").alias("max_nhr")
).collect()[0]
frequency_features = patient_voice_data.select("f0_mean", "f0_std", "formant1", "formant2").toPandas()
spectral_centroid = np.mean(frequency_features['f0_mean'])
formant_ratio = np.mean(frequency_features['formant2'] / frequency_features['formant1'])
voice_quality_score = calculate_voice_quality_index(jitter_analysis['mean_jitter'], shimmer_analysis['mean_shimmer'], nhr_analysis['mean_nhr'])
trend_analysis = patient_voice_data.orderBy("recording_date").select("jitter", "shimmer", "nhr").toPandas()
jitter_trend = np.polyfit(range(len(trend_analysis)), trend_analysis['jitter'], 1)[0]
return JsonResponse({
'status': 'success',
'patient_id': patient_id,
'jitter_statistics': {
'mean': float(jitter_analysis['mean_jitter']),
'std': float(jitter_analysis['std_jitter']),
'min': float(jitter_analysis['min_jitter']),
'max': float(jitter_analysis['max_jitter'])
},
'shimmer_statistics': {
'mean': float(shimmer_analysis['mean_shimmer']),
'std': float(shimmer_analysis['std_shimmer']),
'min': float(shimmer_analysis['min_shimmer']),
'max': float(shimmer_analysis['max_shimmer'])
},
'nhr_statistics': {
'mean': float(nhr_analysis['mean_nhr']),
'std': float(nhr_analysis['std_nhr']),
'min': float(nhr_analysis['min_nhr']),
'max': float(nhr_analysis['max_nhr'])
},
'frequency_analysis': {
'spectral_centroid': float(spectral_centroid),
'formant_ratio': float(formant_ratio)
},
'voice_quality_score': float(voice_quality_score),
'trend_slope': float(jitter_trend)
})
def multidimensional_correlation_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
analysis_period = data.get('period', '30')
correlation_threshold = data.get('threshold', 0.5)
feature_selection = data.get('features', ['temperature', 'humidity', 'jitter', 'shimmer'])
climate_disease_data = spark.read.option("header", "true").csv("hdfs://localhost:9000/integrated_data/")
filtered_data = climate_disease_data.filter(
datediff(current_date(), col("record_date")) <= int(analysis_period)
)
correlation_pairs = []
for i in range(len(feature_selection)):
for j in range(i+1, len(feature_selection)):
feature1, feature2 = feature_selection[i], feature_selection[j]
correlation_df = filtered_data.select(feature1, feature2).toPandas()
correlation_value = correlation_df[feature1].corr(correlation_df[feature2])
correlation_pairs.append({
'feature1': feature1,
'feature2': feature2,
'correlation': float(correlation_value)
})
significant_correlations = [pair for pair in correlation_pairs if abs(pair['correlation']) >= correlation_threshold]
climate_impact_analysis = filtered_data.groupBy("climate_category").agg(
avg("jitter").alias("avg_jitter"),
avg("shimmer").alias("avg_shimmer"),
avg("disease_severity").alias("avg_severity"),
count("patient_id").alias("patient_count")
).collect()
seasonal_pattern = filtered_data.withColumn("month", month("record_date")).groupBy("month").agg(
avg("temperature").alias("avg_temp"),
avg("humidity").alias("avg_humidity"),
avg("disease_severity").alias("avg_severity")
).orderBy("month").collect()
feature_importance = calculate_feature_importance(filtered_data.toPandas(), feature_selection)
cluster_analysis = perform_clustering_analysis(filtered_data.select(*feature_selection).toPandas())
return JsonResponse({
'status': 'success',
'analysis_period': analysis_period,
'total_records': filtered_data.count(),
'correlation_matrix': correlation_pairs,
'significant_correlations': significant_correlations,
'climate_impact': [
{
'climate_category': row['climate_category'],
'avg_jitter': float(row['avg_jitter']),
'avg_shimmer': float(row['avg_shimmer']),
'avg_severity': float(row['avg_severity']),
'patient_count': row['patient_count']
} for row in climate_impact_analysis
],
'seasonal_patterns': [
{
'month': row['month'],
'avg_temperature': float(row['avg_temp']),
'avg_humidity': float(row['avg_humidity']),
'avg_severity': float(row['avg_severity'])
} for row in seasonal_pattern
],
'feature_importance': feature_importance,
'cluster_results': cluster_analysis
})
def calculate_voice_quality_index(jitter, shimmer, nhr):
normalized_jitter = min(jitter * 100, 10)
normalized_shimmer = min(shimmer * 100, 10)
normalized_nhr = min(nhr * 100, 10)
quality_index = 100 - (normalized_jitter + normalized_shimmer + normalized_nhr)
return max(quality_index, 0)
def calculate_feature_importance(data_df, features):
correlations = data_df[features].corrwith(data_df['disease_severity']).abs()
importance_dict = {feature: float(correlations[feature]) for feature in features}
return importance_dict
def perform_clustering_analysis(feature_data):
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaled_data = scaler.fit_transform(feature_data)
kmeans = KMeans(n_clusters=3, random_state=42)
clusters = kmeans.fit_predict(scaled_data)
cluster_centers = kmeans.cluster_centers_
return {
'cluster_labels': clusters.tolist(),
'cluster_centers': cluster_centers.tolist(),
'inertia': float(kmeans.inertia_)
}
基于大数据的气候驱动的疾病传播系统文档展示
💖💖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
