一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，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。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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
大数据框架：Hadoop+Spark（Hive需要定制修改） 开发语言：Java+Python（两个版本都支持） 数据库：MySQL 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持） 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery
基于大数据的气候驱动的疾病传播可视化分析系统是一个融合了现代大数据处理技术与医疗健康数据分析的综合性平台。系统采用Hadoop分布式存储架构和Spark大数据处理引擎作为核心技术支撑，通过Python语言进行数据处理与算法实现，结合Django后端框架构建稳定的服务层，前端使用Vue框架配合ElementUI组件库和Echarts可视化库打造直观的用户交互界面。系统主要功能涵盖气候数据与疾病传播数据的采集存储、综合风险评估模型构建、驱动因素关联性分析、地理空间分布展示、时间序列变化趋势分析等核心模块，通过HDFS分布式文件系统存储海量气候与疾病数据，利用Spark SQL进行高效的数据查询与处理，借助Pandas和NumPy等科学计算库实现复杂的统计分析算法，最终通过MySQL数据库管理系统信息和用户数据，形成一个完整的从数据采集、处理分析到可视化展示的闭环系统，为疾病预防控制和公共卫生决策提供数据支持和分析工具。
三、基于大数据的气候驱动的疾病传播可视化分析系统-视频解说
985高校毕设标杆：气候驱动疾病传播可视化系统Spark+Vue+MySQL技术方案解析|系统
四、基于大数据的气候驱动的疾病传播可视化分析系统-功能展示
五、基于大数据的气候驱动的疾病传播可视化分析系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, avg, max, min, stddev, corr, lag, lead, date_add, date_sub
from pyspark.sql.window import Window
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import json
spark = SparkSession.builder.appName("ClimateDiseasePredictionSystem").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def comprehensive_risk_assessment(climate_data, disease_data, region_id, start_date, end_date):
climate_df = spark.read.format("parquet").load(climate_data)
disease_df = spark.read.format("parquet").load(disease_data)
filtered_climate = climate_df.filter((col("region_id") == region_id) & (col("date") >= start_date) & (col("date") <= end_date))
filtered_disease = disease_df.filter((col("region_id") == region_id) & (col("date") >= start_date) & (col("date") <= end_date))
climate_stats = filtered_climate.agg(avg("temperature").alias("avg_temp"), avg("humidity").alias("avg_humidity"), avg("precipitation").alias("avg_precip"), stddev("temperature").alias("temp_stddev")).collect()[0]
disease_stats = filtered_disease.agg(avg("case_count").alias("avg_cases"), max("case_count").alias("max_cases"), min("case_count").alias("min_cases")).collect()[0]
temp_risk = 0.0
if climate_stats["avg_temp"] > 30:
temp_risk = min((climate_stats["avg_temp"] - 30) / 10, 1.0)
elif climate_stats["avg_temp"] < 10:
temp_risk = min((10 - climate_stats["avg_temp"]) / 15, 1.0)
humidity_risk = 0.0
if climate_stats["avg_humidity"] > 70:
humidity_risk = min((climate_stats["avg_humidity"] - 70) / 30, 1.0)
elif climate_stats["avg_humidity"] < 30:
humidity_risk = min((30 - climate_stats["avg_humidity"]) / 30, 1.0)
precip_risk = min(climate_stats["avg_precip"] / 100, 1.0) if climate_stats["avg_precip"] > 50 else 0.0
stability_risk = climate_stats["temp_stddev"] / 10 if climate_stats["temp_stddev"] else 0.0
disease_trend_risk = 0.0
if disease_stats["max_cases"] > 0:
disease_trend_risk = min(disease_stats["avg_cases"] / disease_stats["max_cases"], 1.0)
comprehensive_risk = (temp_risk * 0.3 + humidity_risk * 0.25 + precip_risk * 0.2 + stability_risk * 0.15 + disease_trend_risk * 0.1)
risk_level = "低风险"
if comprehensive_risk > 0.7:
risk_level = "高风险"
elif comprehensive_risk > 0.4:
risk_level = "中风险"
return {"comprehensive_risk": round(comprehensive_risk, 3), "risk_level": risk_level, "temp_risk": round(temp_risk, 3), "humidity_risk": round(humidity_risk, 3), "precip_risk": round(precip_risk, 3), "stability_risk": round(stability_risk, 3), "disease_trend_risk": round(disease_trend_risk, 3)}
def driving_factor_analysis(climate_data, disease_data, region_id, start_date, end_date):
climate_df = spark.read.format("parquet").load(climate_data)
disease_df = spark.read.format("parquet").load(disease_data)
merged_df = climate_df.join(disease_df, ["region_id", "date"], "inner").filter((col("region_id") == region_id) & (col("date") >= start_date) & (col("date") <= end_date))
temp_corr = merged_df.select(corr("temperature", "case_count").alias("correlation")).collect()[0]["correlation"]
humidity_corr = merged_df.select(corr("humidity", "case_count").alias("correlation")).collect()[0]["correlation"]
precip_corr = merged_df.select(corr("precipitation", "case_count").alias("correlation")).collect()[0]["correlation"]
wind_speed_corr = merged_df.select(corr("wind_speed", "case_count").alias("correlation")).collect()[0]["correlation"]
air_pressure_corr = merged_df.select(corr("air_pressure", "case_count").alias("correlation")).collect()[0]["correlation"]
window_spec = Window.partitionBy("region_id").orderBy("date")
lag_analysis_df = merged_df.withColumn("temperature_lag1", lag("temperature", 1).over(window_spec)).withColumn("temperature_lag3", lag("temperature", 3).over(window_spec)).withColumn("temperature_lag7", lag("temperature", 7).over(window_spec))
temp_lag1_corr = lag_analysis_df.select(corr("temperature_lag1", "case_count").alias("correlation")).collect()[0]["correlation"]
temp_lag3_corr = lag_analysis_df.select(corr("temperature_lag3", "case_count").alias("correlation")).collect()[0]["correlation"]
temp_lag7_corr = lag_analysis_df.select(corr("temperature_lag7", "case_count").alias("correlation")).collect()[0]["correlation"]
correlation_results = {"temperature": temp_corr or 0.0, "humidity": humidity_corr or 0.0, "precipitation": precip_corr or 0.0, "wind_speed": wind_speed_corr or 0.0, "air_pressure": air_pressure_corr or 0.0}
lag_results = {"temperature_lag1": temp_lag1_corr or 0.0, "temperature_lag3": temp_lag3_corr or 0.0, "temperature_lag7": temp_lag7_corr or 0.0}
sorted_factors = sorted(correlation_results.items(), key=lambda x: abs(x[1]), reverse=True)
primary_factor = sorted_factors[0][0] if sorted_factors and abs(sorted_factors[0][1]) > 0.3 else "无显著因素"
factor_strength = "强相关" if abs(sorted_factors[0][1]) > 0.7 else "中等相关" if abs(sorted_factors[0][1]) > 0.4 else "弱相关"
return {"correlation_analysis": correlation_results, "lag_analysis": lag_results, "primary_driving_factor": primary_factor, "factor_strength": factor_strength, "correlation_ranking": sorted_factors}
def geographical_spatial_analysis(climate_data, disease_data, target_date, disease_type):
climate_df = spark.read.format("parquet").load(climate_data)
disease_df = spark.read.format("parquet").load(disease_data)
target_climate = climate_df.filter(col("date") == target_date)
target_disease = disease_df.filter((col("date") == target_date) & (col("disease_type") == disease_type))
spatial_merged = target_climate.join(target_disease, ["region_id"], "left").fillna(0, ["case_count"])
region_stats = spatial_merged.select("region_id", "longitude", "latitude", "temperature", "humidity", "precipitation", "case_count").collect()
hotspot_threshold = spatial_merged.select(avg("case_count").alias("avg_cases")).collect()[0]["avg_cases"] * 1.5
hotspots = []
coldspots = []
for region in region_stats:
if region["case_count"] > hotspot_threshold:
hotspots.append({"region_id": region["region_id"], "longitude": region["longitude"], "latitude": region["latitude"], "case_count": region["case_count"], "temperature": region["temperature"], "humidity": region["humidity"]})
elif region["case_count"] < hotspot_threshold * 0.3:
coldspots.append({"region_id": region["region_id"], "longitude": region["longitude"], "latitude": region["latitude"], "case_count": region["case_count"], "temperature": region["temperature"], "humidity": region["humidity"]})
spatial_clustering_df = spatial_merged.withColumn("case_density", when(col("case_count") > hotspot_threshold, "高密度").when(col("case_count") < hotspot_threshold * 0.3, "低密度").otherwise("中密度"))
density_distribution = spatial_clustering_df.groupBy("case_density").count().collect()
neighbor_analysis = []
for region in region_stats[:10]:
neighbors = [r for r in region_stats if r["region_id"] != region["region_id"] and ((r["longitude"] - region["longitude"])**2 + (r["latitude"] - region["latitude"])**2)**0.5 < 1.0]
neighbor_avg_cases = sum([n["case_count"] for n in neighbors]) / len(neighbors) if neighbors else 0
spatial_autocorr = 1.0 if abs(region["case_count"] - neighbor_avg_cases) < 5 else 0.5 if abs(region["case_count"] - neighbor_avg_cases) < 15 else 0.0
neighbor_analysis.append({"region_id": region["region_id"], "case_count": region["case_count"], "neighbor_avg": neighbor_avg_cases, "spatial_autocorr": spatial_autocorr})
return {"hotspots": hotspots, "coldspots": coldspots, "density_distribution": [{"density": row["case_density"], "count": row["count"]} for row in density_distribution], "neighbor_analysis": neighbor_analysis, "total_regions": len(region_stats), "analysis_date": target_date}
六、基于大数据的气候驱动的疾病传播可视化分析系统-文档展示
七、END
💛💛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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
