💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
基于大数据的中国常见传染病数据分析系统介绍
中国常见传染病数据分析系统是一个基于大数据技术架构的综合性数据分析平台，采用Hadoop分布式文件系统作为底层存储支撑，结合Spark大数据计算引擎实现海量传染病数据的高效处理与分析。系统运用Python作为主要开发语言，Django框架构建后端服务架构，前端采用Vue.js结合ElementUI组件库和Echarts可视化图表库，为用户提供直观友好的数据交互体验。系统核心功能涵盖传染病数据的统一管理、疾病流行病学特征分析、医疗干预措施效果评估、人口特征与疾病关联性研究以及公共卫生政策效能分析等多个维度。通过集成Pandas和NumPy等数据科学库，系统能够对中国各地区常见传染病的发病率、传播规律、季节性变化、人群分布特征进行深度挖掘，并通过Spark SQL实现复杂的多维度关联分析，为疾病预防控制提供数据支撑和决策参考，同时支持动态数据可视化展示，帮助相关人员直观理解传染病流行趋势和防控效果。
基于大数据的中国常见传染病数据分析系统演示视频
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基于大数据的中国常见传染病数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, when, desc, asc, date_format, year, month
from pyspark.sql.types import StructType, StructField, StringType, IntegerType, FloatType, DateType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("DiseaseDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def epidemic_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
disease_type = data.get('disease_type')
start_date = data.get('start_date')
end_date = data.get('end_date')
region = data.get('region', 'all')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("dbtable", "disease_data").option("user", "root").option("password", "123456").load()
if disease_type != 'all':
df = df.filter(col("disease_type") == disease_type)
if start_date and end_date:
df = df.filter((col("report_date") >= start_date) & (col("report_date") <= end_date))
if region != 'all':
df = df.filter(col("region") == region)
monthly_stats = df.select(date_format(col("report_date"), "yyyy-MM").alias("month"), col("case_count"), col("region"), col("age_group"), col("gender")).groupBy("month", "region", "age_group", "gender").agg(sum("case_count").alias("total_cases"), count("*").alias("report_count"), avg("case_count").alias("avg_cases"))
trend_analysis = monthly_stats.groupBy("month").agg(sum("total_cases").alias("monthly_total")).orderBy("month")
region_analysis = monthly_stats.groupBy("region").agg(sum("total_cases").alias("region_total")).orderBy(desc("region_total"))
age_analysis = monthly_stats.groupBy("age_group").agg(sum("total_cases").alias("age_total")).orderBy("age_group")
gender_analysis = monthly_stats.groupBy("gender").agg(sum("total_cases").alias("gender_total"))
peak_period = trend_analysis.orderBy(desc("monthly_total")).first()
high_risk_region = region_analysis.first()
vulnerable_age = age_analysis.orderBy(desc("age_total")).first()
seasonal_pattern = df.select(date_format(col("report_date"), "MM").alias("season_month"), col("case_count")).groupBy("season_month").agg(avg("case_count").alias("avg_seasonal_cases")).orderBy("season_month")
growth_rate_data = trend_analysis.collect()
growth_rates = []
for i in range(1, len(growth_rate_data)):
current = growth_rate_data[i]["monthly_total"]
previous = growth_rate_data[i-1]["monthly_total"]
growth_rate = ((current - previous) / previous) * 100 if previous != 0 else 0
growth_rates.append({"month": growth_rate_data[i]["month"], "growth_rate": round(growth_rate, 2)})
result_data = {"trend_data": [{"month": row["month"], "total": row["monthly_total"]} for row in trend_analysis.collect()], "region_data": [{"region": row["region"], "total": row["region_total"]} for row in region_analysis.collect()], "age_data": [{"age_group": row["age_group"], "total": row["age_total"]} for row in age_analysis.collect()], "gender_data": [{"gender": row["gender"], "total": row["gender_total"]} for row in gender_analysis.collect()], "seasonal_data": [{"month": row["season_month"], "avg_cases": row["avg_seasonal_cases"]} for row in seasonal_pattern.collect()], "peak_info": {"month": peak_period["month"], "cases": peak_period["monthly_total"]}, "high_risk_info": {"region": high_risk_region["region"], "cases": high_risk_region["region_total"]}, "vulnerable_group": {"age_group": vulnerable_age["age_group"], "cases": vulnerable_age["age_total"]}, "growth_analysis": growth_rates}
return JsonResponse({"status": "success", "data": result_data})
def medical_intervention_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
intervention_type = data.get('intervention_type')
evaluation_period = data.get('evaluation_period', 30)
target_region = data.get('target_region')
intervention_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("dbtable", "intervention_data").option("user", "root").option("password", "123456").load()
disease_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("dbtable", "disease_data").option("user", "root").option("password", "123456").load()
if intervention_type:
intervention_df = intervention_df.filter(col("intervention_type") == intervention_type)
if target_region:
intervention_df = intervention_df.filter(col("region") == target_region)
disease_df = disease_df.filter(col("region") == target_region)
intervention_df = intervention_df.withColumn("intervention_start", col("start_date")).withColumn("intervention_end", col("end_date"))
joined_df = disease_df.join(intervention_df, ["region"], "inner")
before_intervention = joined_df.filter(col("report_date") < col("intervention_start")).groupBy("region", "intervention_type").agg(avg("case_count").alias("before_avg_cases"), sum("case_count").alias("before_total_cases"), count("*").alias("before_report_count"))
during_intervention = joined_df.filter((col("report_date") >= col("intervention_start")) & (col("report_date") <= col("intervention_end"))).groupBy("region", "intervention_type").agg(avg("case_count").alias("during_avg_cases"), sum("case_count").alias("during_total_cases"), count("*").alias("during_report_count"))
after_intervention = joined_df.filter(col("report_date") > col("intervention_end")).groupBy("region", "intervention_type").agg(avg("case_count").alias("after_avg_cases"), sum("case_count").alias("after_total_cases"), count("*").alias("after_report_count"))
effectiveness_analysis = before_intervention.join(during_intervention, ["region", "intervention_type"], "outer").join(after_intervention, ["region", "intervention_type"], "outer")
effectiveness_results = effectiveness_analysis.collect()
analysis_results = []
for row in effectiveness_results:
before_avg = row["before_avg_cases"] or 0
during_avg = row["during_avg_cases"] or 0
after_avg = row["after_avg_cases"] or 0
immediate_effect = ((before_avg - during_avg) / before_avg) * 100 if before_avg > 0 else 0
sustained_effect = ((before_avg - after_avg) / before_avg) * 100 if before_avg > 0 else 0
intervention_efficiency = (row["during_total_cases"] or 0) / (row["during_report_count"] or 1)
recovery_rate = ((during_avg - after_avg) / during_avg) * 100 if during_avg > 0 else 0
analysis_results.append({"region": row["region"], "intervention_type": row["intervention_type"], "before_period": {"avg_cases": before_avg, "total_cases": row["before_total_cases"] or 0}, "during_period": {"avg_cases": during_avg, "total_cases": row["during_total_cases"] or 0}, "after_period": {"avg_cases": after_avg, "total_cases": row["after_total_cases"] or 0}, "effectiveness_metrics": {"immediate_reduction": round(immediate_effect, 2), "sustained_reduction": round(sustained_effect, 2), "intervention_efficiency": round(intervention_efficiency, 2), "recovery_rate": round(recovery_rate, 2)}})
cost_benefit_df = intervention_df.join(disease_df.groupBy("region").agg(avg("case_count").alias("baseline_cases")), ["region"], "inner")
cost_effectiveness = cost_benefit_df.select("region", "intervention_type", "intervention_cost", "baseline_cases").collect()
cost_analysis = []
for cost_row in cost_effectiveness:
cost_per_case_prevented = cost_row["intervention_cost"] / (cost_row["baseline_cases"] or 1)
cost_analysis.append({"region": cost_row["region"], "intervention_type": cost_row["intervention_type"], "total_cost": cost_row["intervention_cost"], "cost_per_case_prevented": round(cost_per_case_prevented, 2)})
return JsonResponse({"status": "success", "effectiveness_data": analysis_results, "cost_analysis": cost_analysis})
def population_disease_correlation_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
analysis_dimension = data.get('analysis_dimension', 'age_gender')
target_disease = data.get('target_disease')
correlation_threshold = data.get('correlation_threshold', 0.5)
population_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("dbtable", "population_data").option("user", "root").option("password", "123456").load()
disease_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/disease_db").option("dbtable", "disease_data").option("user", "root").option("password", "123456").load()
if target_disease:
disease_df = disease_df.filter(col("disease_type") == target_disease)
correlation_df = disease_df.join(population_df, ["region", "age_group", "gender"], "inner")
age_correlation = correlation_df.groupBy("age_group", "disease_type").agg(avg("case_count").alias("avg_cases"), sum("case_count").alias("total_cases"), avg("population_density").alias("avg_density"), avg("economic_level").alias("avg_economic"))
gender_correlation = correlation_df.groupBy("gender", "disease_type").agg(avg("case_count").alias("avg_cases"), sum("case_count").alias("total_cases"), count("*").alias("case_frequency"))
region_socioeconomic = correlation_df.groupBy("region", "disease_type").agg(avg("case_count").alias("avg_cases"), avg("education_level").alias("avg_education"), avg("income_level").alias("avg_income"), avg("healthcare_accessibility").alias("avg_healthcare"))
occupation_risk = correlation_df.groupBy("occupation_type", "disease_type").agg(sum("case_count").alias("total_cases"), count("*").alias("exposure_frequency"), avg("case_count").alias("avg_risk_level"))
lifestyle_factors = correlation_df.select("lifestyle_score", "case_count", "disease_type", "age_group").groupBy("disease_type", "age_group").agg(avg("lifestyle_score").alias("avg_lifestyle"), avg("case_count").alias("avg_cases"))
age_risk_profile = age_correlation.collect()
age_analysis = []
for age_row in age_risk_profile:
risk_score = (age_row["avg_cases"] * age_row["avg_density"]) / (age_row["avg_economic"] or 1)
age_analysis.append({"age_group": age_row["age_group"], "disease_type": age_row["disease_type"], "average_cases": age_row["avg_cases"], "total_cases": age_row["total_cases"], "population_density": age_row["avg_density"], "economic_factor": age_row["avg_economic"], "calculated_risk_score": round(risk_score, 3)})
gender_risk_profile = gender_correlation.collect()
gender_analysis = []
for gender_row in gender_risk_profile:
gender_susceptibility = gender_row["total_cases"] / (gender_row["case_frequency"] or 1)
gender_analysis.append({"gender": gender_row["gender"], "disease_type": gender_row["disease_type"], "average_cases": gender_row["avg_cases"], "total_cases": gender_row["total_cases"], "susceptibility_index": round(gender_susceptibility, 3)})
socioeconomic_impact = region_socioeconomic.collect()
socio_analysis = []
for socio_row in socioeconomic_impact:
health_disparity_index = socio_row["avg_cases"] / ((socio_row["avg_education"] * socio_row["avg_income"] * socio_row["avg_healthcare"]) or 1)
socio_analysis.append({"region": socio_row["region"], "disease_type": socio_row["disease_type"], "average_cases": socio_row["avg_cases"], "education_level": socio_row["avg_education"], "income_level": socio_row["avg_income"], "healthcare_access": socio_row["avg_healthcare"], "disparity_index": round(health_disparity_index, 4)})
occupational_risk = occupation_risk.collect()
occupation_analysis = []
for occ_row in occupational_risk:
occupational_hazard_ratio = occ_row["total_cases"] / (occ_row["exposure_frequency"] or 1)
occupation_analysis.append({"occupation": occ_row["occupation_type"], "disease_type": occ_row["disease_type"], "total_cases": occ_row["total_cases"], "exposure_frequency": occ_row["exposure_frequency"], "average_risk": occ_row["avg_risk_level"], "hazard_ratio": round(occupational_hazard_ratio, 3)})
lifestyle_correlation = lifestyle_factors.collect()
lifestyle_analysis = []
for lifestyle_row in lifestyle_correlation:
lifestyle_correlation_coefficient = lifestyle_row["avg_lifestyle"] / (lifestyle_row["avg_cases"] or 1) if lifestyle_row["avg_cases"] > 0 else 0
lifestyle_analysis.append({"disease_type": lifestyle_row["disease_type"], "age_group": lifestyle_row["age_group"], "lifestyle_score": lifestyle_row["avg_lifestyle"], "average_cases": lifestyle_row["avg_cases"], "correlation_strength": round(lifestyle_correlation_coefficient, 3)})
high_risk_groups = [item for item in age_analysis if item["calculated_risk_score"] > correlation_threshold]
vulnerable_populations = [item for item in socio_analysis if item["disparity_index"] > correlation_threshold]
return JsonResponse({"status": "success", "age_risk_analysis": age_analysis, "gender_risk_analysis": gender_analysis, "socioeconomic_analysis": socio_analysis, "occupational_risk_analysis": occupation_analysis, "lifestyle_correlation_analysis": lifestyle_analysis, "high_risk_identification": high_risk_groups, "vulnerable_population_identification": vulnerable_populations})
基于大数据的中国常见传染病数据分析系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
