大数据可视化毕业设计：基于人类健康数据的Echarts图表系统开发

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基于人类健康数据的Echarts图表系统开发-系统功能介绍

本系统是一套完整的基于大数据技术的人类健康生活方式数据分析与可视化平台，采用Hadoop+Spark大数据处理框架作为核心技术架构，结合Python数据分析和Java后端开发双重技术栈实现。系统通过Spark SQL对海量健康数据进行深度挖掘和统计分析，运用Pandas和NumPy进行数据预处理和科学计算，实现对人群健康画像、生活方式影响因子、特定人群风险评估以及生理衰退关联性等四大维度共18个细分分析场景的全面覆盖。前端采用Vue+ElementUI构建现代化用户界面，集成Echarts图表库实现多样化的数据可视化展示，包括柱状图、折线图、饼图、散点图以及热力图等多种图表类型，直观呈现年龄分布、BMI健康状况、血压血糖水平、慢性病患病情况、饮食运动习惯影响分析、不同人群健康差异对比以及生理指标随年龄变化趋势等分析结果。系统后端基于Django或SpringBoot框架搭建RESTful API接口，使用MySQL数据库存储结构化数据，通过HDFS分布式文件系统管理大规模健康数据集，实现从数据采集、清洗、分析到可视化展示的完整数据处理流水线。

基于人类健康数据的Echarts图表系统开发-系统技术介绍

大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL

基于人类健康数据的Echarts图表系统开发-系统背景意义

随着现代社会生活节奏加快和生活方式多样化，人们的健康状况呈现出复杂多变的特征，传统的健康数据分析方法已难以满足大规模、多维度健康信息处理的需求。当前医疗健康领域积累了大量包含个人生理指标、生活习惯、慢性病史等多元化数据，这些数据蕴含着丰富的健康规律和生活方式影响模式，但由于数据量庞大、结构复杂，传统数据库和分析工具在处理效率和分析深度方面存在明显局限。大数据技术的快速发展为解决这一问题提供了新的技术路径，Hadoop分布式存储和Spark内存计算框架能够有效处理海量健康数据，而现代可视化技术则可以将复杂的分析结果以直观易懂的图表形式展现。在健康中国战略背景下，运用信息技术手段深入挖掘健康数据价值，探索生活方式与健康状况之间的内在联系，已成为健康管理和疾病预防领域的重要发展方向。 本课题的实际意义主要体现在为健康数据分析提供了一套完整的大数据技术解决方案，通过系统化的数据处理流程和多维度分析模型，能够帮助相关从业人员更好地理解人群健康状况分布特征和生活方式影响规律。系统建立的18个具体分析维度涵盖了从人群画像到个体风险评估的完整分析链条，为健康管理机构制定针对性的健康指导方案提供数据支撑。技术层面，本课题将Hadoop、Spark等主流大数据技术与健康数据分析场景相结合，为同类型大数据应用项目提供了可参考的技术架构和实施方案。可视化展示模块通过多种图表类型将复杂的统计分析结果转化为直观的视觉信息，降低了数据解读的专业门槛，有助于提升健康知识的普及效果。从教育角度看，本系统整合了大数据处理、数据分析、Web开发等多个技术领域的知识点，对于理解大数据技术在垂直领域的应用具有一定的学习参考价值，同时也为相关专业学生提供了一个较为完整的综合性实践项目案例。

基于人类健康数据的Echarts图表系统开发-系统演示视频

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基于人类健康数据的Echarts图表系统开发-系统演示图片

基于人类健康数据的Echarts图表系统开发-系统部分代码

from pyspark.sql import SparkSession

from pyspark.sql.functions import col, avg, count, when, split, regexp_extract

import pandas as pd

import numpy as np

from django.http import JsonResponse

from django.views import View

import json

spark = SparkSession.builder.appName("HealthDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

class HealthProfileAnalysisView(View):

    def post(self, request):

        df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/health_data/Train.csv")

        age_bins = [0, 25, 35, 45, 55, 65, 100]

        age_labels = ['青年(18-25)', '青壮年(26-35)', '中年(36-45)', '中老年(46-55)', '老年(56-65)', '高龄(65+)']

        df_age = df.select(col("Age (years)").alias("age"))

        age_distribution = []

        for i in range(len(age_bins)-1):

            count_result = df_age.filter((col("age") >= age_bins[i]) & (col("age") < age_bins[i+1])).count()

            age_distribution.append({"age_group": age_labels[i], "count": count_result, "percentage": round(count_result / df.count() * 100, 2)})

        bmi_analysis = df.select(col("BMI").alias("bmi")).withColumn("bmi_category", when(col("bmi") < 18.5, "偏瘦").when((col("bmi") >= 18.5) & (col("bmi") < 24), "正常").when((col("bmi") >= 24) & (col("bmi") < 28), "超重").otherwise("肥胖")).groupBy("bmi_category").agg(count("*").alias("count")).collect()

        bmi_results = [{"category": row["bmi_category"], "count": row["count"], "percentage": round(row["count"] / df.count() * 100, 2)} for row in bmi_analysis]

        blood_pressure_df = df.select(split(col("Blood Pressure (s/d)"), "/").alias("bp_split")).select(col("bp_split")[0].cast("int").alias("systolic"), col("bp_split")[1].cast("int").alias("diastolic"))

        bp_analysis = blood_pressure_df.withColumn("bp_category", when((col("systolic") < 120) & (col("diastolic") < 80), "正常").when((col("systolic") < 140) & (col("diastolic") < 90), "偏高").otherwise("高血压")).groupBy("bp_category").agg(count("*").alias("count")).collect()

        bp_results = [{"category": row["bp_category"], "count": row["count"], "percentage": round(row["count"] / blood_pressure_df.count() * 100, 2)} for row in bp_analysis]

        glucose_analysis = df.select(col("Blood Glucose Level (mg/dL)").alias("glucose")).withColumn("glucose_category", when(col("glucose") < 100, "理想").when((col("glucose") >= 100) & (col("glucose") < 126), "边缘").otherwise("高风险")).groupBy("glucose_category").agg(count("*").alias("count")).collect()

        glucose_results = [{"category": row["glucose_category"], "count": row["count"], "percentage": round(row["count"] / df.count() * 100, 2)} for row in glucose_analysis]

        chronic_diseases = df.select(col("Chronic Diseases").alias("diseases")).filter(col("diseases").isNotNull() & (col("diseases") != "None")).groupBy("diseases").agg(count("*").alias("count")).orderBy(col("count").desc()).collect()

        disease_results = [{"disease": row["diseases"], "count": row["count"], "percentage": round(row["count"] / df.count() * 100, 2)} for row in chronic_diseases[:10]]

        return JsonResponse({"age_distribution": age_distribution, "bmi_analysis": bmi_results, "blood_pressure": bp_results, "glucose_levels": glucose_results, "chronic_diseases": disease_results}, safe=False)

class LifestyleImpactAnalysisView(View):

    def post(self, request):

        df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/health_data/Train.csv")

        diet_impact = df.select(col("Diet").alias("diet"), col("BMI").alias("bmi"), col("Blood Glucose Level (mg/dL)").alias("glucose")).filter(col("diet").isNotNull()).groupBy("diet").agg(avg("bmi").alias("avg_bmi"), avg("glucose").alias("avg_glucose"), count("*").alias("count")).collect()

        diet_results = [{"diet_type": row["diet"], "avg_bmi": round(row["avg_bmi"], 2), "avg_glucose": round(row["avg_glucose"], 2), "sample_count": row["count"]} for row in diet_impact]

        activity_bp_df = df.select(split(col("Blood Pressure (s/d)"), "/").alias("bp_split"), col("Physical Activity Level").alias("activity")).select(col("bp_split")[0].cast("int").alias("systolic"), col("bp_split")[1].cast("int").alias("diastolic"), col("activity"))

        activity_impact = activity_bp_df.select(col("activity"), col("systolic"), col("diastolic"), col("Cholesterol Level (mg/dL)").alias("cholesterol")).filter(col("activity").isNotNull()).groupBy("activity").agg(avg("systolic").alias("avg_systolic"), avg("diastolic").alias("avg_diastolic"), avg("cholesterol").alias("avg_cholesterol"), count("*").alias("count")).collect()

        activity_results = [{"activity_level": row["activity"], "avg_systolic": round(row["avg_systolic"], 2), "avg_diastolic": round(row["avg_diastolic"], 2), "avg_cholesterol": round(row["avg_cholesterol"], 2), "sample_count": row["count"]} for row in activity_impact]

        smoking_bp_df = df.select(split(col("Blood Pressure (s/d)"), "/").alias("bp_split"), col("Smoking Status").alias("smoking")).select(col("bp_split")[0].cast("int").alias("systolic"), col("bp_split")[1].cast("int").alias("diastolic"), col("smoking"))

        smoking_impact = smoking_bp_df.select(col("smoking"), col("systolic"), col("diastolic"), col("BMI").alias("bmi")).filter(col("smoking").isNotNull()).groupBy("smoking").agg(avg("systolic").alias("avg_systolic"), avg("diastolic").alias("avg_diastolic"), avg("bmi").alias("avg_bmi"), count("*").alias("count")).collect()

        smoking_results = [{"smoking_status": row["smoking"], "avg_systolic": round(row["avg_systolic"], 2), "avg_diastolic": round(row["avg_diastolic"], 2), "avg_bmi": round(row["avg_bmi"], 2), "sample_count": row["count"]} for row in smoking_impact]

        alcohol_impact = df.select(col("Alcohol Consumption").alias("alcohol"), col("Cholesterol Level (mg/dL)").alias("cholesterol"), col("Blood Glucose Level (mg/dL)").alias("glucose")).filter(col("alcohol").isNotNull()).groupBy("alcohol").agg(avg("cholesterol").alias("avg_cholesterol"), avg("glucose").alias("avg_glucose"), count("*").alias("count")).collect()

        alcohol_results = [{"alcohol_level": row["alcohol"], "avg_cholesterol": round(row["avg_cholesterol"], 2), "avg_glucose": round(row["avg_glucose"], 2), "sample_count": row["count"]} for row in alcohol_impact]

        sleep_impact = df.select(col("Sleep Patterns").alias("sleep"), col("Stress Levels").alias("stress"), col("Cognitive Function").alias("cognitive")).filter(col("sleep").isNotNull()).groupBy("sleep").agg(avg("stress").alias("avg_stress"), avg("cognitive").alias("avg_cognitive"), count("*").alias("count")).collect()

        sleep_results = [{"sleep_pattern": row["sleep"], "avg_stress": round(row["avg_stress"], 2), "avg_cognitive": round(row["avg_cognitive"], 2), "sample_count": row["count"]} for row in sleep_impact]

        return JsonResponse({"diet_impact": diet_results, "activity_impact": activity_results, "smoking_impact": smoking_results, "alcohol_impact": alcohol_results, "sleep_impact": sleep_results}, safe=False)

class AgeRelatedAnalysisView(View):

    def post(self, request):

        df = spark.read.option("header", "true").option("inferSchema", "true").csv("hdfs://localhost:9000/health_data/Train.csv")

        age_bins = [0, 30, 40, 50, 60, 70, 100]

        age_labels = ['20-30岁', '31-40岁', '41-50岁', '51-60岁', '61-70岁', '70岁以上']

        age_bone_data = []

        for i in range(len(age_bins)-1):

            age_group_df = df.filter((col("Age (years)") >= age_bins[i]) & (col("Age (years)") < age_bins[i+1]))

            avg_bone_density = age_group_df.select(avg(col("Bone Density (g/cm²)")).alias("avg_bone")).collect()[0]["avg_bone"]

            sample_count = age_group_df.count()

            age_bone_data.append({"age_group": age_labels[i], "avg_bone_density": round(avg_bone_density, 3), "sample_count": sample_count})

        age_vision_data = []

        for i in range(len(age_bins)-1):

            age_group_df = df.filter((col("Age (years)") >= age_bins[i]) & (col("Age (years)") < age_bins[i+1]))

            avg_vision = age_group_df.select(avg(col("Vision Sharpness")).alias("avg_vision")).collect()[0]["avg_vision"]

            avg_hearing = age_group_df.select(avg(col("Hearing Ability (dB)")).alias("avg_hearing")).collect()[0]["avg_hearing"]

            sample_count = age_group_df.count()

            age_vision_data.append({"age_group": age_labels[i], "avg_vision": round(avg_vision, 2), "avg_hearing": round(avg_hearing, 2), "sample_count": sample_count})

        age_cognitive_data = []

        for i in range(len(age_bins)-1):

            age_group_df = df.filter((col("Age (years)") >= age_bins[i]) & (col("Age (years)") < age_bins[i+1]))

            avg_cognitive = age_group_df.select(avg(col("Cognitive Function")).alias("avg_cognitive")).collect()[0]["avg_cognitive"]

            sample_count = age_group_df.count()

            age_cognitive_data.append({"age_group": age_labels[i], "avg_cognitive_score": round(avg_cognitive, 2), "sample_count": sample_count})

        numeric_columns = ["Age (years)", "BMI", "Blood Glucose Level (mg/dL)", "Cholesterol Level (mg/dL)", "Bone Density (g/cm²)", "Vision Sharpness", "Hearing Ability (dB)", "Cognitive Function", "Stress Levels"]

        correlation_data = []

        age_col = df.select(col("Age (years)").alias("age")).rdd.map(lambda x: x.age).collect()

        for col_name in numeric_columns[1:]:

            col_data = df.select(col(col_name)).rdd.map(lambda x: x[0]).collect()

            correlation = np.corrcoef(age_col, col_data)[0, 1]

            correlation_data.append({"indicator": col_name, "correlation_with_age": round(correlation, 3)})

        return JsonResponse({"age_bone_analysis": age_bone_data, "age_sensory_analysis": age_vision_data, "age_cognitive_analysis": age_cognitive_data, "age_correlation_matrix": correlation_data}, safe=False)

基于人类健康数据的Echarts图表系统开发-结语

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