前言

💖💖作者：计算机程序员小杨
💙💙个人简介：我是一名计算机相关专业的从业者，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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，大家有技术代码这一块的问题可以问我！
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一.开发工具简介

大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
开发语言：Python
后端框架：Django
前端：Vue
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL

二.系统内容简介

基于大数据的道路交通事故数据可视化分析系统采用了Hadoop与Spark作为核心的大数据处理框架，通过HDFS实现海量交通事故数据的分布式存储，利用Spark SQL完成数据的快速查询与计算任务。系统后端使用Django框架搭建，前端采用Vue技术栈开发，结合Pandas和NumPy进行数据预处理与统计分析，MySQL数据库负责存储结构化的分析结果。平台提供了数据大屏展示功能，能够直观呈现交通事故的整体态势，在系统首页集成了多维度的数据概览模块，交通道路事故数据管理模块支持对原始数据的查看与筛选操作。事故原因分析模块从多个维度挖掘导致事故发生的关键因素，事故严重程度分析通过伤亡人数、财产损失等指标进行分级统计，司机特征分析关注驾龄、年龄、性别等驾驶员属性与事故的关联性。环境因素分析涵盖天气状况、路面条件、光照情况等外部条件对事故的影响，时间维度分析从年、月、日、时等不同时间粒度揭示事故发生的规律性，车辆因素分析则聚焦车型、车龄、车辆状态等车辆本身的特征，通过这样的方式为交通管理部门提供数据支撑。

三.系统功能演示

基于大数据的道路交通事故数据可视化分析系统–演示视频

四.系统界面展示

五.系统源码展示

from pyspark.sql import SparkSession
from pyspark.sql.functions import col,count,avg,sum,when,year,month,dayofmonth,hour,date_format
from django.http import JsonResponse
from django.views import View
import pandas as pd
import numpy as np
from datetime import datetime
import json
spark=SparkSession.builder.appName("GallstoneAnalysis").config("spark.sql.warehouse.dir","/user/hive/warehouse").config("spark.executor.memory","2g").config("spark.driver.memory","1g").getOrCreate()
class DiseaseDataAnalysisView(View):
    def get(self,request):
        hdfs_path="hdfs://localhost:9000/medical_data/gallstone_records.csv"
        df=spark.read.csv(hdfs_path,header=True,inferSchema=True)
        df=df.filter(col("disease_type").isNotNull())
        df=df.withColumn("diagnosis_year",year(col("diagnosis_date")))
        df=df.withColumn("diagnosis_month",month(col("diagnosis_date")))
        yearly_stats=df.groupBy("diagnosis_year").agg(count("patient_id").alias("patient_count"),avg("age").alias("avg_age"),sum(when(col("severity_level")=="severe",1).otherwise(0)).alias("severe_cases"))
        yearly_data=yearly_stats.toPandas()
        yearly_result=[]
        for index,row in yearly_data.iterrows():
            year_info={"year":int(row["diagnosis_year"]),"total_patients":int(row["patient_count"]),"average_age":round(float(row["avg_age"]),2),"severe_count":int(row["severe_cases"]),"severe_rate":round(float(row["severe_cases"])/float(row["patient_count"])*100,2) if row["patient_count"]>0 else 0}
            yearly_result.append(year_info)
        cause_distribution=df.groupBy("primary_cause").agg(count("patient_id").alias("cause_count")).orderBy(col("cause_count").desc())
        cause_data=cause_distribution.toPandas()
        total_cause_count=cause_data["cause_count"].sum()
        cause_result=[]
        for index,row in cause_data.iterrows():
            cause_item={"cause_name":row["primary_cause"],"count":int(row["cause_count"]),"percentage":round(float(row["cause_count"])/float(total_cause_count)*100,2) if total_cause_count>0 else 0}
            cause_result.append(cause_item)
        age_groups=df.withColumn("age_group",when(col("age")<30,"30岁以下").when((col("age")>=30)&(col("age")<50),"30-50岁").when((col("age")>=50)&(col("age")<70),"50-70岁").otherwise("70岁以上"))
        age_stats=age_groups.groupBy("age_group","gender").agg(count("patient_id").alias("group_count"))
        age_data=age_stats.toPandas()
        age_result={}
        for index,row in age_data.iterrows():
            age_key=row["age_group"]
            if age_key not in age_result:
                age_result[age_key]={"male":0,"female":0,"total":0}
            if row["gender"]=="男":
                age_result[age_key]["male"]=int(row["group_count"])
            elif row["gender"]=="女":
                age_result[age_key]["female"]=int(row["group_count"])
            age_result[age_key]["total"]+=int(row["group_count"])
        response_data={"status":"success","yearly_analysis":yearly_result,"cause_distribution":cause_result,"age_gender_analysis":age_result,"data_update_time":datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
        return JsonResponse(response_data,safe=False,json_dumps_params={"ensure_ascii":False})
class SeverityPredictionView(View):
    def post(self,request):
        request_data=json.loads(request.body)
        patient_age=request_data.get("age")
        patient_gender=request_data.get("gender")
        symptom_duration=request_data.get("symptom_duration")
        complication_count=request_data.get("complication_count")
        hdfs_training_path="hdfs://localhost:9000/medical_data/training_dataset.csv"
        training_df=spark.read.csv(hdfs_training_path,header=True,inferSchema=True)
        training_df=training_df.filter((col("age").isNotNull())&(col("severity_level").isNotNull()))
        similar_cases=training_df.filter((col("age")>=patient_age-5)&(col("age")<=patient_age+5)&(col("gender")==patient_gender))
        severity_stats=similar_cases.groupBy("severity_level").agg(count("patient_id").alias("level_count"))
        severity_data=severity_stats.toPandas()
        total_similar=severity_data["level_count"].sum()
        severity_prob={}
        for index,row in severity_data.iterrows():
            level_name=row["severity_level"]
            probability=float(row["level_count"])/float(total_similar) if total_similar>0 else 0
            severity_prob[level_name]=round(probability*100,2)
        risk_score=0
        if patient_age>60:
            risk_score+=25
        elif patient_age>45:
            risk_score+=15
        else:
            risk_score+=5
        if symptom_duration>180:
            risk_score+=30
        elif symptom_duration>90:
            risk_score+=20
        else:
            risk_score+=10
        risk_score+=complication_count*15
        if risk_score>70:
            predicted_level="severe"
            suggestion="建议尽快安排详细检查，考虑制定治疗方案"
        elif risk_score>40:
            predicted_level="moderate"
            suggestion="需要定期复查，注意观察症状变化情况"
        else:
            predicted_level="mild"
            suggestion="保持健康生活习惯，按时进行常规体检"
        complication_df=training_df.filter(col("complication_list").isNotNull())
        complication_analysis=complication_df.groupBy("complication_list").agg(count("patient_id").alias("comp_count")).orderBy(col("comp_count").desc()).limit(5)
        complication_data=complication_analysis.toPandas()
        common_complications=[]
        for index,row in complication_data.iterrows():
            comp_item={"name":row["complication_list"],"occurrence":int(row["comp_count"])}
            common_complications.append(comp_item)
        prediction_result={"predicted_severity":predicted_level,"risk_score":risk_score,"severity_probability":severity_prob,"medical_suggestion":suggestion,"common_complications":common_complications,"analysis_time":datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
        return JsonResponse(prediction_result,json_dumps_params={"ensure_ascii":False})
class EnvironmentFactorView(View):
    def get(self,request):
        region_param=request.GET.get("region","all")
        hdfs_env_path="hdfs://localhost:9000/medical_data/environment_factors.csv"
        env_df=spark.read.csv(hdfs_env_path,header=True,inferSchema=True)
        env_df=env_df.filter(col("region").isNotNull()&col("incidence_rate").isNotNull())
        if region_param!="all":
            env_df=env_df.filter(col("region")==region_param)
        region_stats=env_df.groupBy("region").agg(avg("incidence_rate").alias("avg_incidence"),avg("water_hardness").alias("avg_water_hardness"),avg("dietary_fat_intake").alias("avg_fat_intake"),count("record_id").alias("sample_count"))
        region_data=region_stats.toPandas()
        region_analysis=[]
        for index,row in region_data.iterrows():
            region_info={"region_name":row["region"],"average_incidence":round(float(row["avg_incidence"]),4),"water_hardness":round(float(row["avg_water_hardness"]),2) if pd.notna(row["avg_water_hardness"]) else None,"fat_intake_level":round(float(row["avg_fat_intake"]),2) if pd.notna(row["avg_fat_intake"]) else None,"sample_size":int(row["sample_count"])}
            region_analysis.append(region_info)
        correlation_df=env_df.select("water_hardness","dietary_fat_intake","incidence_rate").toPandas()
        correlation_df=correlation_df.dropna()
        if len(correlation_df)>0:
            water_corr=np.corrcoef(correlation_df["water_hardness"],correlation_df["incidence_rate"])[0,1]
            fat_corr=np.corrcoef(correlation_df["dietary_fat_intake"],correlation_df["incidence_rate"])[0,1]
        else:
            water_corr=0
            fat_corr=0
        seasonal_df=env_df.withColumn("season",when(col("month").isin([12,1,2]),"冬季").when(col("month").isin([3,4,5]),"春季").when(col("month").isin([6,7,8]),"夏季").otherwise("秋季"))
        seasonal_stats=seasonal_df.groupBy("season").agg(avg("incidence_rate").alias("season_incidence"),count("record_id").alias("season_count"))
        seasonal_data=seasonal_stats.toPandas()
        seasonal_result=[]
        for index,row in seasonal_data.iterrows():
            season_item={"season":row["season"],"incidence":round(float(row["season_incidence"]),4),"record_count":int(row["season_count"])}
            seasonal_result.append(season_item)
        lifestyle_factors=env_df.groupBy("exercise_frequency","smoking_status").agg(avg("incidence_rate").alias("lifestyle_incidence"))
        lifestyle_data=lifestyle_factors.toPandas()
        lifestyle_result=[]
        for index,row in lifestyle_data.iterrows():
            lifestyle_item={"exercise_level":row["exercise_frequency"],"smoking":row["smoking_status"],"incidence":round(float(row["lifestyle_incidence"]),4)}
            lifestyle_result.append(lifestyle_item)
        environment_response={"status":"success","region_analysis":region_analysis,"correlation_factors":{"water_hardness_correlation":round(float(water_corr),3),"dietary_fat_correlation":round(float(fat_corr),3)},"seasonal_distribution":seasonal_result,"lifestyle_impact":lifestyle_result,"query_region":region_param,"analysis_timestamp":datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
        return JsonResponse(environment_response,json_dumps_params={"ensure_ascii":False})

六.系统文档展示

结束

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